System Framework Research Articles

Foundations and Applications in Political Theory: An Analytical Study

This study explores the practical applications of political theory, focusing on how fundamental principles are translated into real political actions within governance and decision-making contexts. The first section addresses the identification of core principles such as democracy, justice, equality, sovereignty, legitimacy, and rights, which serve as a theoretical framework for political systems and policy formulation. Each principle is examined in depth, highlighting its significance in shaping governance and social order. For instance, democracy emphasizes citizen participation and accountability, while justice relates to fairness and equitable distribution of resources. Various political scenarios where these principles are applied are discussed, including legislative debates, policy-making processes, electoral campaigns, diplomatic negotiations, and judicial rulings. Each scenario illustrates the practical implications of political theories, demonstrating how lawmakers, policymakers, and diplomats engage with these concepts to address societal issues, Furthermore, the study emphasizes the necessity of considering stakeholders' perspectives, including government officials, political parties, civil society organizations, interest groups, and citizens, in evaluating the effectiveness of political actions. Through a critical analysis of these applications, the study assesses the outcomes of political decisions and governance practices, focusing on their alignment with democratic values and the protection of individual rights. It also addresses challenges such as institutional barriers, conflicting interests, and resource constraints that hinder the realization of political ideals. Ultimately, the study offers recommendations to enhance the significance of political theory in contemporary political discourse, advocating for collaborative efforts between theorists and practitioners to ensure that theoretical ideas inform practical policymaking. This study underscores the vital role of political theory in understanding and addressing current political challenges, contributing to enlightened governance and social progress.

Information and resources VA health system leaders need to manage enrollment and retention for Post-9/11 veterans.

To understand Veterans Health Administration (VA) leaders' information and resource needs for managing post-9/11 Veterans' VA enrollment and retention. Interviews conducted from March-May 2022 of VA Medical Center (VAMC) leaders (N = 27) across 15 sites, using stratified sampling based on VAMC characteristics: enrollment rates, number of recently separated Veterans in catchment area, and state Medicaid expansion status. Interview questions were developed using Petersen et al.'s Factors Influencing Choice of Healthcare System framework as a guide. Interviews were transcribed verbatim, and two coders analyzed the interviews using Atlas.ti, a qualitative software program. Coders followed the qualitative coding philosophy developed by Crabtree and Miller, a process of developing codes for salient concepts as they are identified during the analysis process. Two coders analyzed 22% (N = 6) of the interviews and discussed and adjudicated any discrepancies. One coder independently coded the remainder of the interviews. Several key themes were identified regarding facilitators and barriers for VA enrollment including reputation for high-quality VA care, convenience of VA services, awareness of VA services and benefits, and VA mental health services. Nearly every VA leader actively used tools and data to understand enrollment and retention rates and sought to enroll and retain more Veterans. To improve the management of enrollment and retention, VA leaders would like data shared in an easily understandable format and the capability to share data between the VA and community healthcare systems. Enrollment and retention information is important for healthcare leaders to guide their health system decisions. Various tools are currently being used to try to understand the data. However, a multifunctional tool is needed to better aggregate the data to provide VA leadership with key information on Veterans' enrollment and retention.

Retraction Note: Proposed framework for cancelable face recognition system

Retraction Note: Proposed framework for cancelable face recognition system

The Elimination of Rupture: Social Authority's Attempt to Transcend Binary Distinctions

As a representative work of Coleman's theory of rational action, "Foundations of Social Theory" contains Coleman's ideological intention of building a bridge between the macro and the micro, in which the theory of social authority, as an integral part of the theory of rational action, is also an attempt to construct the micro-macro dichotomy in the generation of its authority relationship and authority system. Therefore, based on the methodology and research framework of Coleman's rational action theory system, we select the authority idea of the book "The Foundations of Social Theory" as the object of exposition, and try to comprehensively discuss Coleman's social authority idea through analyzing its middle-level theory of social authority from the aspects of foundation of social authority theory, the granting of authority, the social performance of authority and the overall characteristics of Coleman's social authority thoughts. It seeks to reveal Coleman's attempt to transcend the binary distinction between micro and macro at the authority level.

Rural Environmental Governance: A Communal Irrigation System in Greece through the Social–Ecological System Framework

This article delves into the enduring self-governing nature of a traditional irrigation system in a rural community in Greece through the lens of the social–ecological system (SES) framework. The aim is to analyze the variables and interactions that have sustained this community-based governance structure over a century. This study underscores the critical role of social capital, where social networks and effective communication facilitate self-governance in the management of this common pool resource. The findings reveal how actors coordinate social and physical capital when performing daily activities, enabling them to collectively solve common dilemmas. By examining the institutional arrangements and operational dynamics of the irrigation system, the study illuminates the community-led social processes that drive its sustainability. This analysis enhances the understanding of the importance of integrating social and physical capital in governance through collective action to achieve resilience. This study provides insights into alternative pathways for building resilience and discusses an irrigation system as a communal autonomous space that prioritizes social equity, democratic decision-making, and social–ecological contacts.

Indicator assessment of groundwater resource sustainability: Using the framework of socio-ecological systems in Hamedan - Bahar Plain, Iran

Study areaIran. Study focusHamedan-Bahar plain is one of the important plains in the west of Iran, which is facing a shortage of water resources and excessive extraction of groundwater resources. The purpose of this article is to identify and evaluate the status of sustainability governance indicators using the knowledge of local experts, which can be an effective step towards the sustainable management of groundwater resources. In this paper, we apply Ostrom’s Social-Ecological Systems Framework (SESF) as a diagnostic tool with 52 indicators to assess the sustainability of groundwater resources. New hydrological insights for the regionBased on key informant interviews with local experts and stakeholders, we use the TOPSIS technique and the Shannon Entropy methodologies to weigh and rank indicators influencing sustainability. Findings revealed that the indicators within the category Resource Systems (RS) and Resource Units (RU) with values of 0.74, and 0.70, are the most stable contributing factors to local sustainability, respectively. In contrast, the Governance System (GS), Actor (A), and Interaction (I) first-tier variables were evaluated as less stable, along with Outcomes (O). This suggests that social factors and diverse outcomes may need further attention in the region to ensure management and policy development that can better enable sustainable outcomes. This analysis also demonstrates the usefulness of a comprehensive science-based framework for organizing, analyzing, and presenting a wide range of complex information to inform policymakers and planners.

Integrating reliability analysis into MBSE for FPGA-based safety critical I&C system design in nuclear power plants

Abstract With the widespread application of Field Programmable Gate Array (FPGA) technology in the instrumentation and control (I&C) systems of nuclear power plants (NPPs), its design reliability has become the key to ensuring the subsequent safe operation of NPPs. However, the traditional document-based design method is no longer sufficient to meet the current design needs of complex systems, and there is a lack of research related to the reliability and security analysis of FPGA-based systems. Therefore, the overall goal of this study is to integrate reliability analysis into the model-based systems engineering (MBSE)-based design of FPGA-based I&C systems. Based on the typical application characteristics of the FPGA, a digital design framework for FPGA-based systems is proposed to realize a traceable design process from requirements to implementation. Subsequently, a model-based forward comprehensive reliability analysis method is proposed, which uses a SysML-based mapping mechanism to integrate Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods into the design process, so as to comprehensively identify and analyze failure modes from systems to components and optimize and improve system design. Finally, the proposed method is applied to the analog output design of FPGA-based systems. The proposed method can provide guidance for improving the reliability of FPGA-based DI&C systems and provide theoretical basis for the further engineering application.

Establishment and application of real-time monitoring system for mine ventilation resistance

The monitoring of ventilation resistance in domestic coal mines is generally faced with two problems: huge workload and lagging monitoring data behind production, which is due to the backward monitoring technology. In recent years, with the progress of technology and equipment in the coal industry, ventilation resistance monitoring products have appeared in the market one after another, but they still face the following main problems: (1) the sensor measurement error needs to be further improved; (2) The monitoring scheme is relatively simple, and the use of absolute pressure multi-parameter monitoring faces two problems: measurement error and construction cost; (3) There is a lack of successful application cases of ventilation resistance monitoring in the industry, and the analysis and application of ventilation resistance monitoring data by the upper computer is insufficient. In view of the above problems, this paper studies the principle, monitoring scheme, system framework and practical application of ventilation resistance monitoring, aiming to solve the problem of real-time online monitoring and analysis of ventilation resistance.

Spatial digital twin framework for overheight vehicle warning and re-routing system

Overhead road obstacles present a significant logistical hazard to the heavy vehicle industry. Traditional overheight vehicle warning systems such as passive warning systems (PWS) and active warning systems (AWS) have not adequately reduced the frequency and impact of overheight incidents, encouraging transportation agencies to employ intelligent transport system (ITS) strategies using state-of-the-art advanced technologies. This research takes an innovative approach in developing an immersive user-focused experience, harnessing multi-disciplinary methods and tools to engineer a spatial digital twin prototype for a novel Internet-of-Things (IoT)-based active warning alert and re-routing system (AWARS). LiDAR and 3D GIS were used to model the complex road environment, tailored to the strict fiscal objectives sought by economically mindful organisations. Tree crowns were extracted from near-Infrared aerial imagery and digital elevation models, supplying the dimensions necessary for 3D tree modelling. IoT connectivity was configured using a real-time analytics approach to deliver alerts and re-routing options. The World Traffic Service with live and predictive traffic data was used for the routing application programming interface (API). A standard-configuration common rigid truck (CRT) was inserted into the 3D road environment model to simulate overheight collisions and to ascertain the effect of re-routing on estimated time of arrival (ETA). Longer ETA durations were observed for routes computed by the digital twin. Theoretically, enhanced situational awareness and subsequent reduction of risk likelihood suggests an optimised response to industry demands, despite extended travel times, cultivating a favorable impact on the supply chain through enhanced safety management.

The Framework and Practice of the Official Accountability System in Macao

The Framework and Practice of the Official Accountability System in Macao

A low-storage synchronization framework for blockchain systems

The advent of blockchain technology has brought major changes to traditional centralized storage. Therefore, various fields have begun to study the application and development of blockchain. However, blockchain technology has a serious shortcoming of data bloating. The reason is that blockchain technology achieves decentralization by storing complete blockchain data at each node, incurring a significant amount of blockchain data. Therefore, each node must spend significant amount of storage space and initialization synchronization time. To solve the above problems, in this research, we propose a secure and agile synchronization framework for low storage blockchains. First, we design a K-extreme segment algorithm, which reduces the synchronization time by returning only the first and last k blocks of each block segment at once to the local storage. Next, we decentrally store the block data of the blockchain by IPFS and establish a backup mechanism by IPFS-cluster. Finally, due to use of distributed storage, the nodes must request un-stored block data from IPFS, causing an increase in the throughput of the blockchain network. To avoid network congestion, we propose the working set algorithm to improve the hit ratio of the local storage and reduce the number of requests to decrease throughput. In summary, our experiments demonstrate that the ratio of full nodes to low storage nodes is significantly lower for nodes with higher storage limits compared to those with lower storage limits. In other words, a higher storage limit results in more low storage nodes which can be permitted to ensure that the blockchain network is robust and reliable. Therefore, our proposed framework can provide reliable low storage nodes for the blockchain. The node can reduce the local storage pressure and can still maintain the full functionality of blockchains.

Optimization of resource allocation strategy for high-speed railway based on deep reinforcement learning

With the accelerated development of high-speed railway (HSR), the contradiction between the surge of user services and the demand for resource has become increasingly prominent. Mobile edge computing (MEC) has emerged to improve performance, reduce communication delay and ease network load. In this paper, we design a multi-user MEC system framework that aims to solve the joint optimization problem of computation offloading and resource allocation in HSR communication scenario with deep reinforcement learning algorithm. The framework dynamically allocates computation resource and network bandwidth through the real-time distance between users and base station (BS) to achieve optimal resource utilization and maximize user experience. To achieve this goal, we use a deep reinforcement learning based dynamic computation offloading and resource allocation (DDCORA) optimization algorithm. The algorithm minimizes the system cost by sharing state information among different users and making collaborative decisions to rationally allocate spectrum resource and computation resource. Simulation results show that DDCORA algorithm can significantly decrease the system cost while enhancing the overall system performance and user experience.

Application-based big data development framework for health sciences libraries.

The study is a part of a student research project on performance-based evaluation of big data application in health sciences libraries. It presents a theoretical framework of the performance-based evaluation system for health institutes' libraries in the digital environment. The health sciences libraries' systematic approach was judged based on the five main components (data culture, organisational infrastructure, responsibilities, skills and technology competence) of big data analytics (BDA). A comprehensive literature review of the published studies was undertaken related to BDA, including the diffusion of innovation theory, and the theoretical background of the technology acceptance model to produce an application-based big data development framework for the health sciences libraries. The application-based evaluation model integrates BDA in health sciences libraries for improving library services and performance. The study proposed a need for skilled professionals with the knowledge and experience both professionally and technically. Finally, the study proposed a model that will help to measure the organisation's ability to analyse vast amounts of data to empirically validate the association concerning big data analysis and analytical practices in health libraries.

Marine Ecological Damage Assessment Model for Offshore Oil Extraction

<p>With the progress of society and the development of the economy, people’s awareness of environmental protection is also becoming stronger. Therefore, the damage caused by marine oil spills caused by offshore oil extraction to marine ecology is also receiving more and more attention from people. Marine ecological damage assessment involves various disciplines such as environmental chemistry, biology, toxicology, and hydrodynamics, and requires a large amount of data support, making it a challenge in the field of marine claims. To quantitatively evaluate marine oil spill accidents, the Spill Impact Model Application Package model is used as the system framework to build a marine ecological damage assessment model. The MIKE21 model is used for physical fate simulation, while the biological effects model is used for resource damage assessment of the ecological environment and aquatic organisms. The model is validated using the oil spill accident in Dongshan Bay as an example. The validation results show that the total loss of aquatic organisms under conditions 1 and 2 is 67.30 tons and 56.03 tons, respectively. Under condition 1, the ecological environment of coral and mangroves is affected by an area of 8.805km2 and 5.859km2, respectively, resulting in a loss of 19.5168 million yuan in ecological resources. Under condition 2, the ecological environment of coral and mangroves is affected by an area of 7.646 km2 and 13.515 km2, respectively, resulting in a loss of 26.322 million yuan in ecological resources. In summary, it can be seen that the research on marine ecological damage assessment models for offshore oil extraction provides certain reference value for the field of marine ecological damage assessment in China.</p>

A unified component-based data-driven framework to support interoperability in the healthcare systems

Healthcare organizations must urgently prioritize interoperability to enhance the quality of care they provide. However, achieving this collaboration comes with numerous challenges, including differing approaches, data formats, and standards, as well as concerns about privacy, security, technical complexity, and legal and regulatory issues. To tackle these challenges, we determined a set of interoperability solutions. We also developed a comprehensive, component-based, data-driven framework for healthcare systems. Our study's approach involved three main steps: first, conducting a literature review to gather interoperability requirements and solutions from online databases and grey literature; second, carrying out a qualitative study to develop a framework based on the review results and focus group discussions; and third, using the Delphi method to validate the framework with experts. We extracted information from 36 articles during the screening and assessment process. Based on the proposed framework, we organized the identified themes into various categories, including architecture, architecture components, standards, platforms, policies, data sources, consumers, applications, level of interoperability, healthcare facilities, and considerations. Experts believe that establishing a comprehensive architecture for launching interoperability between health information systems can greatly facilitate this process. All framework components (totaling 197) received unanimous approval. The landscape of healthcare delivery is shifting from a focus on diseases to a patient-centered, data-driven approach. There is a growing demand for personalized healthcare systems, which necessitates increased interoperability among all healthcare stakeholders, particularly when dealing with diverse types of data. Our framework is designed to facilitate the implementation of various types of interoperability in healthcare systems.

Rethinking Australian democracy as a deliberative system

This paper examines the relationship between Australia’s Indigenous and settler colonial systems of democracy through the lens of deliberative systems theory. It suggests that the ongoing effects of colonialism have rendered Indigenous democracy largely invisible causing a harmful divide in Australia’s democracies. A pluralist conception of democracy is necessary to understand the disconnect between the two systems, evidenced by a striking absence of literature on Australian Indigenous democracy. In response, this paper first theorizes a conceptual framework of a concurrent deliberative system, then describes the Indigenous deliberative system and the colonial system’s efforts to eliminate Indigenous democracy. Against this theoretical and empirical background, it considers whether the recent referendum for an Indigenous Voice to Parliament was just a colonial legacy or represented a pathway towards a shared postcolonial democratic future.

Collaborative leadership to empower nurses to implement ABCDE emergency nursing in an emergency department in Nepal

PurposeEmergency medicine can save lives and in 2018 the World Health Assembly passed resolution 72.16 ensuring the role of emergency care in all health systems. With a continued global shortage of emergency physicians, with many low-medium-income countries (LMIC) still to develop emergency medicine as a speciality, the role of emergency nurses is critical to deliver the WHO Emergency Care System Framework (WHO, 2018). Emergency medicine doctors play a critical role in collaborating with nurses, in emergency medicine where nurses are often the first clinicians are often the first clinicians to interact with patients in emergency care settings, making up the majority of health-care professionals in LMIC (Mamalelala, 2024). Yet emergency nursing has yet to become established in Nepal, where nurses are often recruited to emergency departments, without having received any training in emergency or critical care treatment and management. The purpose of this paper is to outline a collaborative leadership approach to co-design an airway, breathing, circulation, disability, exposure (ABCDE) structured approach to an emergency nursing training module designed for nurses to feel empowered in the emergency department and to report on its findings.Design/methodology/approachThis study draws upon mixed methodology research, enrolling 30 nurses (n = 30) from an emergency department in a tertiary hospital in Nepal through three stages of the project: Stage 1: training module co-design, collaborative leadership exploring the rationale for a training module and core features of design based on the ABCDE of emergency medicine; Stage 2: quantitative data were collected to assess baseline pre- and post-intervention knowledge and follow-up knowledge assessment at 30 and 45 days; Stage 3: qualitative data were collected with 24/30 (80%) nurses to evaluate the impact and application of the nurses ABCDE learning 7 months post-training. The qualitative survey was undertaken using online Google Forms.FindingsNurses were fully engaged in the co-design and collaboration of the development of an ABCDE training module which was delivered over 3 h. Full engagement was secured from all nurses in the department, and there were statistically significant advances in ABCDE emergency knowledge from the baseline, however, this knowledge began to decrease at 30 and 45 days. A follow-up qualitative survey was distributed to nurses seven months after training with an 80% return rate, which reported a range of examples of how nurses were continuing to apply their learning in practice.Originality/valueThis training module for emergency nurses was designed collaboratively from the “bottom up” in a tertiary hospital in Nepal, recognising the need to develop emergency nursing in the emergency department. The data revealed promising findings, while knowledge decreased from the post-training questionnaire, qualitative evidence revealed significant changes in practice, with the greatest reported change in the management of the airway. While this training module has made a difference in the quality of care provided, there is a need for a country-wide strategy in this area otherwise it is likely that such an initiative will only be developed by hospitals at a local level (Lecky, 2014). Education and training initiatives for nurses that focus on an evidence-based approach to clinical practice can bridge the workforce gap in the short term, however, the Government of Nepal must decide on establishing a recognised post-graduate sub-specialty in emergency nursing, the duration of training, who should be trained and what curriculum should be followed (Lecky, 2014).

Societies with fission-fusion dynamics as complex adaptive systems: the importance of scale.

In this article, we argue that social systems with fission-fusion (FF) dynamics are best characterized within a complex adaptive systems (CAS) framework. We discuss how different endogenous and exogenous factors drive scale-dependent network properties across temporal, spatial and social domains. Importantly, this view treats the dynamics themselves as objects of study, rather than variously defined notions of static 'social groups' that have hitherto dominated thinking in behavioural ecology. CAS approaches allow us to interrogate FF dynamics in taxa that do not conform to more traditional conceptualizations of sociality and encourage us to pose new types of questions regarding the sources of stability and change in social systems, distinguishing regular variations from those that would lead to system-level reorganization. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Retraction Note: Research on campus network security protection system framework based on cloud data and intrusion detection algorithm

Retraction Note: Research on campus network security protection system framework based on cloud data and intrusion detection algorithm

Optimizing Maintenance Resource Scheduling and Site Selection for Urban Metro Systems: A Multi-Objective Approach to Enhance System Resilience

This study developed an optimization model for the strategic location of maintenance resource supply sites and the scheduling of multiple resources following failures in urban metro systems, with the objective of enhancing system resilience. The model employs a multi-objective optimization framework, focusing primarily on minimizing resource scheduling time and reducing costs. It incorporates critical factors such as spatial location, network topology, station size, and passenger flow. A hybrid method, combining the non-dominated sorting genetic algorithm III and the technique for order of preference by similarity to ideal solution, is used to solve the model, with its effectiveness confirmed through a case study of the Nanjing Metro system. The simulation results yielded an optimal number of 21 maintenance resource supply stations and provided their placement. In the event of large-scale failures, the optimal resource scheduling strategy ensures demand satisfaction rates exceed 90% at critical stations, maintaining an overall rate of 87.09%, therefore significantly improving resource scheduling efficiency and the system’s emergency response capabilities and enhancing the physical resilience and recovery capabilities of the urban metro system. Moreover, the model accounts for economic factors, striving to balance emergency response capabilities with production continuity and cost efficiency through effective maintenance strategies and resource utilization. This approach provides a systematic framework for urban metro systems to manage sudden failures, ensuring rapid recovery to normal operations and minimizing operational disruptions in scenarios of limited resources.