Implementation Of Framework Research Articles

Implementation of the theoretical framework of the circular economy in the tourism sector

ABSTRACT Tourism has led economic growth in much of the world in the aftermath of the global economic crisis. The circular economy (CE) is an ideal alternative to address the challenges that the sector must face to achieve a more sustainable model. Currently, the theoretical framework of CE has not yet been adapted to the tourism context. This paper fills this research gap by adapting the general framework to the specificities of this sector. The main contributions of this work are the definition of CE applied to the tourism field, as well as the adaptation of CE principles and strategies to tourism activity. Another relevant contribution is that 220 indicators have been analysed to assess their suitability for measuring the level of circularity of tourism activity. In addition, hospitality, food services, transport, leisure activities, and intermediation activities have been considered throughout the process. This is a remarkable novelty given that no previous work is known to examine the provision of the tourism product as a whole. The results show that in adapting CE to tourism, it is essential to consider the complexities of this sector, all stakeholders, and the different levels of the tourism value chain.

A Spatial‐Based Quantum Graph Convolutional Neural Network and Its Full‐Quantum Circuit Implementation

AbstractWith the rapid advancement of quantum computing, the exploration of quantum graph neural networks is gradually emerging. However, the absence of a circuit framework for quantum implementation and limited physical qubits hinder their realization on real quantum computers. To address these challenges, this paper proposes a spatial‐based quantum graph convolutional neural network and implements it on a superconducting quantum computer. Specifically, this model exclusively consists of quantum circuits, including quantum aggregation circuits in the quantum graph convolutional layer and quantum classification circuits in the quantum dense layer. To meet the requirements of Noisy Intermediate‐Scale Quantum computing, a first‐order extraction method to reduce circuit size is employed. Experimental results in node classification tasks demonstrate that this model achieves comparable or even superior performance compared to classical graph neural networks while utilizing fewer parameters. Therefore, this model can inspire further advancements in quantum graph neural networks and facilitate their implementation on physical quantum devices.

The Invisible Middle: Latino Men in Higher Education Student Affairs Master’s Programs

ABSTRACT This qualitative narrative inquiry study examined the lived experiences of 21 Latino men enrolled in higher education and student affairs (HESA) administration master’s-level graduate programs and how they made meaning of their social identities. Through the implementation of a socio-ecological outcomes framework, findings described how social identities informed academic engagement, relationships with faculty of Color, and recommendations for HESA graduate programs.

Artificial Intelligence in Waste Sorting: Advancing Recycling Processes in Greece Through Ai-Driven Solutions

The integration of Artificial Intelligence (AI) in waste sorting presents a transformative opportunity to enhance recycling processes, addressing inefficiencies and environmental challenges. This study investigates the application of AI-driven technologies within Greece, focusing on improving material classification, reducing contamination, and optimizing waste management practices. By leveraging advanced image recognition, machine learning algorithms, and robotic systems, the research demonstrates AI's potential to overcome infrastructure deficiencies and high operational costs, while fostering sustainability. A comprehensive analysis identifies socio-economic and environmental benefits, evaluates current barriers, and proposes a scalable framework for AI implementation. The findings aim to guide policymakers, industry stakeholders, and environmental organizations in adopting AI as a pivotal tool for advancing waste management and achieving global sustainability targets.

Collaborative capacity building for strengthening rehabilitation services in Sabah, Malaysia: a partnership between university hospital and state health department.

This paper aims to outline the foundational framework for strengthening rehabilitation services in Sabah, Malaysia, through a collaborative capacity-building initiative between Hospital Universiti Malaysia Sabah (HUMS) and the Sabah State Health Department (JKNS). By focusing on academic, research, and service capacity-building, this partnership seeks to address the rehabilitation needs of the local population, particularly for individuals with functional limitations. The collaboration integrates HUMS's academic and clinical strengths with JKNS's existing rehabilitation services. Key components include developing postgraduate training for rehabilitation medicine, expanding community-based rehabilitation outreach services, and establishing a referral network between hospitals and community healthcare providers. The partnership has resulted in the implementation of a comprehensive framework that enhances academic capacity, fosters research collaboration, and improves rehabilitation service delivery across Sabah. This approach is aligned with the WHO's Rehabilitation 2030 initiative, advocating for stronger integration of rehabilitation into healthcare systems. The collaborative efforts between HUMS and JKNS demonstrate the critical role of partnerships between academic institutions and public health departments in strengthening rehabilitation services. This model offers a replicable strategy for influencing policy development and ensuring resource allocation to meet the growing rehabilitation needs in underserved regions.

Facilitators and barriers to reach and enrollment into a medically tailored meals program within a section 1115 Medicaid pilot: clinic staff perspectives

IntroductionMedically tailored meals (MTMs) are home-delivered, nutritionally tailored meals for individuals living with complex or advanced diet-sensitive medical conditions. In 2020, Massachusetts Medicaid implemented the Flexible Services Program (FSP) through a Section 1115 Demonstration, which funded novel nutrition programs, including MTMs, for high-risk patients through Accountable Care Organizations (ACOs). Little is known from the practitioners’ perspective regarding the facilitators and barriers to reaching and enrolling patients in MTM programs.MethodsWe interviewed 19 staff across four ACOs that had implemented MTM interventions. Interviews were conducted from Feb to Aug 2023 and included staff who participated in patient screening, referral, or enrollment. The interview guide was informed by the Health Equity Implementation Framework. Interviews were recorded and transcribed and coded using NVivo software. We used directed qualitative content analyses. The study team identified and discussed common themes and presented them back to our ACO partners.ResultsStaff described facilitators of and barriers to reach and enrollment related to several domains of the Health Equity Implementation Framework. For program (innovation) factors, facilitators included perceived positive effects on patient health outcomes and a relative advantage over both the status quo and other nutrition assistance programs; outreach by care team members rather than other staff; the eligibility criteria, which were viewed as appropriate and evidence-based; and the simplicity of the program, which aided communication with patients. Patient-related facilitators included patients being more in need of the program due to more severe illness and being more motivated to change dietary behaviors. Patient-related barriers included lacking a working phone or stable housing and concern about meals meeting taste and cultural food preferences. Staff-related barriers included limited time and especially knowledge about the MTM program.DiscussionThis study highlights the perspectives of front-line staff during the implementation of an MTM program in a state-wide 1,115 Demonstration. Staff may require multiple trainings to gain full knowledge about the program and increase self-efficacy in describing it with sensitivity. These new findings elevate voices from front-line healthcare staff in MTM delivery and can help inform strategies for effective, equitable implementation of MTM programs.

Rethinking Instructional Leadership in Pakistan’s Elite Schools: A Call for Indigenous Leadership Models

This opinion paper makes a case for the need to develop indigenous instructional leadership models through Pakistani elite schools that challenge the use of Western leadership models. This reveals some significant cultural misalignments between the imported leadership models and the local educational scenario, needing leadership techniques rooted in Pakistani norms and high academic standards. This paper presents the fundamental building blocks of indigenous models of leadership that are culturally, organisationally, and contextually relevant to Pakistan. We discuss an implementation framework that guides awareness of obstacles and stakeholder roles and underscores the importance of systematic evaluation mechanisms. The study proposes recommendations with respect to policymaking, research initiatives and capacity-building programmes that will facilitate effective transformation. The paper argues that indigenous leadership models could contribute to educational effectiveness by bringing academic excellence and cultural authenticity to educational leadership and potentially influencing educational leadership practices outside elite schools to the advantage of Pakistan’s educational system.

On Integrating Mixed Reality within University Curricula

<span id="docs-internal-guid-b917b8d8-7fff-3bd4-23e3-5fc79a729e14"><span>The paper describes the architecture and implementation of an experimental framework based on a software platform for enhancing practical and theoretical learning capacities among students in different fields of specialisation by using mixed reality. This platform allows students’ effective interaction with a set of experimental models in two ways: by using interactive augmented reality based mobile applications or by using complete immersion. A case study on using this platform within a target group of students from University of Bacău, Romania is provided and some implications of this teaching approach are analysed. The results have empirically proved that using a learning environment based on mixed reality in teaching improves participatory culture, technical knowledge, and learning satisfaction degree of students. This study also emphasises the need for improvements in the integration of this framework to ensure that the technology's use is not limited to specific courses or user groups. </span></span>

Implementation of prehospital point-of-care ultrasound using a novel continuous feedback approach in a UK helicopter emergency medical service

BackgroundThere has been increased use of prehospital point-of-care ultrasound (PoCUS) by helicopter emergency medical services (HEMS) in recent years. Lack of governance structure and evidence of benefit have been described as major barriers to its implementation. This paper describes a novel approach to implementation of prehospital PoCUS and clinical governance framework in a UK HEMS.MethodsA retrospective database review was undertaken at London’s Air Ambulance (LAA) from 1st September 2021 to 31st March 2023. All patients who had PoCUS examination were included. Scans were archived in a cloud-based server and reviewed weekly by expert clinicians. They were graded in adequacy, agreement between reviewer and clinician was recorded and fed back to the clinicians allowing continuous feedback learning. In-hospital diagnosis was sought for patients having the full Pump, Pleura and Pouring blood (PPPB) protocol. Cohen’s Kappa (ƙ) was calculated for inter-rater reliability. Sensitivity and specificity analysis was performed using 2 × 2 tables.ResultsLAA attended 3,068 missions. Our reviewers identified 701 PoCUS scanning encounters and 628 were included in the final analysis. Clinicians performed 420 scans for pneumothorax, 308 for free fluid and 305 pericardial effusions respectively. Majority of the population were male (85%) who sustained traumatic (93.5%) thoracic injuries (65%). Paramedics performed 29% of the scans. Reviewers deemed 83% of the scans of adequate quality. Inter-rater reliability between clinicians and reviewers was 0.6 for pericardial effusion, 0.67 for pneumothorax and 0.71 for free fluid respectively. A full PPPB protocol was performed in 52 patients out of which 46 were included. The sensitivity and specificity of PPPB protocol for diagnosis life-threatening injuries was 0.5 and 0.9 respectively.ConclusionIntroduction of prehospital PoCUS in a HEM service utilizing high quality training, user-friendly workflow and image archiving system, robust governance framework and continuous feedback may be feasible allowing high quality ultrasound examinations. The bespoke PPPB protocol in prehospital may improve diagnosis of life-threatening injuries.

Multi-Hazard Early Warning Systems in the Sendai Framework for Disaster Risk Reduction: Achievements, Gaps, and Future Directions

Abstract Bringing together global efforts to enhance the implementation of warnings in managing vulnerabilities, hazards, risks, and disasters is essential to saving lives and for long-term vulnerability reduction. Ten years into the implementation of the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR), there has been a renewed focus on warnings following the 2022 announcement by the United Nations Secretary-General of the five-year goal of Early Warnings for All. Delivering on Target G of the SFDRR has subsequently generated significant outcomes, however substantial gaps remain with implementing effective early warning systems (EWS). This article charts the policy evolution of warnings within the UN context and outlines the progress and remaining gaps of EWS in the SFDRR to date. Three key gaps that hinder the effective delivery of SFDRR and beyond are identified: (1) the need for common understanding of warning processes and terminology, such as multi-hazard EWS, and further elucidation of indicators used to measure and chart progress; (2) the need to mobilize and strengthen existing EWS, many of which are not formally recognized yet do the work of warnings across actors and entities, especially in fragile or resource-poor contexts; and (3) the need to foster collaboration between the multitude of actors and approaches involved in all forms of warnings, including people-centered warnings to address diversity and inclusivity, and integrate top-down and bottom-up approaches across sectors. Significant barriers to working across the numerous silos (institutional, geographical, political, and scientific) must be overcome to generate effective people-centered multi-hazard EWS to support disaster risk reduction in the future. Recommendations on how to fill these gaps in future frameworks are provided, to support people-centered, integrated warnings for all.

Implementation of the children and young people’s health partnership model of paediatric integrated care: a mixed-methods process evaluation

ObjectiveA process evaluation of the Children and Young People’s Health Partnership (CYPHP) model of integrated care for the interpretation of trial findings and building evidence on the implementation of integrated...

Disparities in Diabetes Distress and Nutrition Management Among Black and Hispanic Adults: A Mixed Methods Exploration of Social Determinants.

The purpose of this study was to explore self-reported diabetes management strategies, social determinants of health (SDOH), and barriers to care among people with diabetes receiving care in a safety-net setting to identify factors contributing to disparities in outcomes for Black and Hispanic adults and inform future interventions. Sequential, explanatory, mixed methods study comprised a survey of adults with diabetes seen in primary care at a safety-net hospital in New England, followed by qualitative semistructured interviews with a subset of the Black and Hispanic respondents. Descriptive statistics, chi-square and t tests were used to analyze quantitative data. The health equity implementation framework was used to guide qualitative data collection and directed content analysis. A total of 496 respondents completed the survey; 48 Black and Hispanic adults participated in interviews. Diabetes-related distress was significantly higher among Black and Hispanic participants compared to White participants. Nutrition management use was significantly lower among Black and Hispanic participants. Qualitative findings suggest that SDOH and lack of education and support, specifically, nutrition and access to self-management resources, contributed to diabetes-related distress and prevented optimal self-management. High rates of diabetes-related distress and low rates of nutrition management were identified in Black and Hispanic adults in a safety-net setting. Qualitative interviews demonstrated a relationship between adverse SDOH and lack of nutrition education with diabetes distress and challenges to self-management, potentially contributing to disparities in outcomes. Findings suggest that increased uptake of nutrition therapy and self-management education and support may be critical for improving diabetes outcomes and promoting health equity.

Cooperation Between Universities and Public Schools Within the Framework of Acceleration-Deceleration Curriculum Implementation

Cooperation Between Universities and Public Schools Within the Framework of Acceleration-Deceleration Curriculum Implementation

Del relato a la conversión: El poder del Storytelling en el marketing digital en Ecuador

Storytelling has become a key tool in digital marketing, capable of significantly influencing brand perception and the customer conversion process. This article aims to analyze the impact of storytelling in digital marketing strategies in Ecuador, a country with a growing adoption of digital technologies and social networks. A qualitative-descriptive approach is employed based on a review of recent literature and success stories of local brands. It discusses the essential components of storytelling, such as authenticity, consistency and the ability to generate emotions, and how these factors enhance connection with consumers and promote engagement. Key metrics for measuring the effectiveness of storytelling campaigns and their impact on the conversion funnel are also examined. The findings highlight that brands that integrate storytelling into their digital strategies achieve greater loyalty and differentiation in the market. Finally, a framework for the effective implementation of storytelling in Ecuadorian digital marketing is proposed, considering the cultural and technological particularities of the environment.

Physics-based numerical implementation framework towardsmuti-scale contact problem

This paper establishes a general computational framework to solve the muti-scale contact problem by integrating the statistical contact model with the finite element format. Compared to existing models, the proposed method is applicable to most geometric configurations and can effectively evaluate the pressure distribution. In this work, a modified Karush-Kuhn-Tucker (KKT) condition is proposed by the assumption that asperity height obeys the Gaussian distribution. Therefore, in the variational formula, the contact contribution is decomposed into body contribution and asperity contribution, corresponding to the nominal smooth surface and roughness, respectively. Then the linearization and constraint enforcement of these two components are derived, followed by a nonlinear Newton-Raphson-based iterative algorithm. The contact patch test and Hertz contact test are conducted, and the predicted results are consistent with the theoretical and experimental values, confirming the effectiveness and accuracy of the proposed approach. It is worth noting that in the Hertz contact test, the contact pressure distribution varies progressively with the roughness level and external force, tending to the Hertz limit or Gaussian limit. This means that the proposed method can be applied to any roughness and load. Finally, the contact behaviors of the transmission interface of a piezoelectric actuator, i.e., a typical multi-scale contact problem, are studied as an engineering application case.

A New Control Approach for a Multi-Controlled Wheelchair Utilizing Deep Learning Framework and Raspberry Pi

This article describes the construction and control mechanisms of a multiple control wheelchair (MCW) aimed to aid individuals with amyotrophic lateral sclerosis (ALS) and lower limb disabilities. The MCW has been tested in the laboratory on a total of 10 patients, among them 3 with lower limb disabilities, 6 elderly patients, and 1 patient with ALS. The proposed wheelchair can navigate in any direction using object detection techniques powered by OpenCV. Employing a deep learning algorithm, it performs real-time object detection and tracks its current position in various environmental conditions. The MCW can be controlled using a joystick, internet of things (IoT), or a combination of both. A novel aspect of this work is the implementation of a deep learning framework on a Raspberry Pi processor, allowing the wheelchair to navigate in any direction based on the user’s needs. The Raspberry Pi interfaces with all sensors, camera, and additional peripherals through serial communication, enabling monitoring and control via an android mobile application. This setup effectively reduces the physical distance between the user and the caregiver. During the test, a range of functional measurements were taken, including assessments of the object detection process and the performance of hardware components such as sensors and motors connected to the system. The current system is unable to precisely determine an object’s position during detection, but ongoing research is focused on enhancing the system’s capability to accurately map objects in 3D space for improved wheelchair navigation.

Designing a Data Governance Framework for Cybersecurity Risk Reporting: A Model for Business Intelligence Teams

In the rapidly evolving digital landscape, the integration of robust data governance practices is crucial for enhancing cybersecurity risk reporting within organizations. This abstract presents a model for designing a data governance framework tailored specifically for Business Intelligence (BI) teams. The proposed framework emphasizes the intersection of data governance and cybersecurity, ensuring that data management practices support comprehensive risk reporting and decision-making processes. Key elements of the framework include data quality management, data access controls, and data lineage tracking, which collectively contribute to more accurate and timely cybersecurity risk assessments. By embedding these elements into the governance structure, BI teams can better manage and mitigate data-related risks, ensuring that cybersecurity threats are identified and addressed proactively. The model also incorporates advanced analytics and machine learning techniques to automate the detection of potential vulnerabilities, thereby enhancing the efficiency and effectiveness of risk reporting. Furthermore, the framework advocates for a collaborative approach, involving stakeholders from IT, security, compliance, and business units to ensure that data governance policies align with the organization's overall cybersecurity strategy. This interdisciplinary collaboration is essential for fostering a culture of cybersecurity awareness and accountability across the enterprise. In addition to technical considerations, the framework addresses the need for clear governance policies and procedures, regular audits, and continuous monitoring to maintain data integrity and compliance with regulatory requirements. The model is designed to be adaptable, allowing organizations to customize their data governance practices based on their specific industry, regulatory environment, and risk profile. The implementation of this data governance framework is expected to significantly improve the accuracy and reliability of cybersecurity risk reporting, providing BI teams with the tools and insights necessary to support informed decision-making and safeguard organizational assets against emerging threats.

A Guide for Implementing DPYD Genotyping for Systemic Fluoropyrimidines into Clinical Practice.

The safety of systemic fluoropyrimidines (e.g., 5-fluorouracil, capecitabine) is impacted by germline genetic variants in DPYD, which encodes the dihydropyrimidine dehydrogenase (DPD) enzyme that functions as the rate-limiting step in the catabolism of this drug class. Genetic testing to identify those with DPD deficiency can help mitigate the risk of severe and life-threatening fluoropyrimidine-induced toxicities. Globally, the integration of DPYD genetic testing into patient care has varied greatly, ranging from being required as the standard of care in some countries to limited clinical use in others. Thus, implementation strategies have evolved differently across health systems and countries. The primary objective of this tutorial is to provide practical considerations and best practice recommendations for the implementation of DPYD-guided systemic fluoropyrimidine dosing. We adapted the Exploration, Preparation, Implementation, and Sustainment (EPIS) framework to cover topics including the clinical evidence supporting DPYD genotyping to guide fluoropyrimidine therapy, regulatory guidance for DPYD genotyping, key stakeholder engagement, logistics for DPYD genotyping, development of point-of-care clinical decision support tools, and considerations for the creation of sustainable and scalable DPYD genotype-integrated workflows. This guide also describes approaches to counseling patients about DPYD testing and result disclosure, along with examples of patient and provider educational resources. Together, DPYD testing and clinical practice integration aim to promote safe prescribing of fluoropyrimidine therapy and decrease the risk of severe and life-threatening fluoropyrimidine toxicities.

Precision Medical Education: Institutional Strategies for Successful Implementation.

Precision medical education (PME) represents a transformative approach in medical education, systematizing individualized and efficient learner competency development for enhanced patient care. While implementing PME has been described conceptually using the P4 medical education framework (proactive, personalized, participatory, predictive), many programs and institutions face implementation questions on navigating data integration complexities, resource constraints, cultural risk aversion to innovation, and more.To address such challenges, the authors of this Commentary propose 5 concrete, concurrent strategies for institutions and training programs to implement PME successfully. Of these strategies, 4 are aligned with the P4 framework. Strategy 1: Curate educational data, adhering to 8 foundational data principles (relevant, trusted, consistent, timely, accessible, connected, interactive, actionable), and integrate it into an education data warehouse to create a comprehensive learner profile. Strategy 2: Build institutional analytic capabilities, including leveraging existing resources and tools, such as artificial intelligence, to turn data into actionable insights. Strategy 3: Establish transparent, accountable governance structures with broad stakeholder engagement, emphasizing principled co-production of PME by learners, faculty, and educational leaders. Strategy 4: Continuously collect and evaluate outcomes tied to PME interventions, building validity evidence through predictive linkages. Strategy 5: Lead change to drive PME forward. This strategy cuts across all 4 other strategies to promote the effective adoption of systems and processes that drive PME into institutional culture.When successfully operationalized, PME enables precise, evidence-informed decision-making about and by learners, while benefiting faculty, coaches, and educational leaders. Through these strategies, programs and institutions can overcome implementation pitfalls and harness the full potential of PME to improve educational and patient care outcomes.

AI-Powered Predictive Analytics for Dynamic Cloud Resource Optimization: A Technical Implementation Framework

This article explores the transformative impact of AI-driven predictive analytics on cloud resource optimization, presenting a comprehensive technical implementation framework. The article shows how artificial intelligence and machine learning architectures revolutionize traditional cloud management approaches through advanced predictive capabilities and dynamic resource allocation. It examines the evolution from static threshold-based systems to sophisticated AI-driven solutions, analyzing their implementation strategies across various organizational contexts. The article delves into multiple optimization domains, including capacity provisioning, cost management, performance enhancement, and energy efficiency, while presenting real-world applications and impact analyses across different industries. Through extensive case studies and empirical evidence, the article demonstrates how organizations leverage AI-powered solutions to address complex cloud resource management challenges, achieve operational efficiencies, and maintain competitive advantages in the digital marketplace. The article also explores future developments and provides strategic recommendations for organizations implementing cloud optimization frameworks, emphasizing the importance of standardized approaches, stakeholder engagement, and sustainable practices in cloud resource management.