Collaborative Resource Management Research Articles

Collaborative Computation Offloading and Resource Management in Space–Air–Ground Integrated Networking: A Deep Reinforcement Learning Approach

With the increasing dissemination of the Internet of Things and 5G, mobile edge computing has become a novel scheme to assist terminal devices in executing computation tasks. To elevate the coverage and computation capability of edge computing, a collaborative computation offloading and resource management architecture was proposed in space–air–ground integrated networking (SAGIN). In this manuscript, we established a novel model considering the computation offloading cost constraints of the communication, computing and cache model in the SAGIN. To be specific, the joint optimization problem of collaborative computation offloading and resource management was modeled as a mixed integer nonlinear programming problem. To address this issue, this paper proposed a computation offloading and resource allocation strategy based on deep reinforcement learning (DRL). Differing from traditional methods, DRL does not need a well-established formulation or previous information, and it is capable of revising the strategy adaptively according to the environment. The simulation results demonstrate the proposed approach can achieve the optimal reward values in the case of different terminal device numbers. Furthermore, this manuscript provided the analysis with variant parameters of the proposed approach.

From Theory to Practice: HRM Practices and Intellectual Capital in Fostering Innovation

In a rapidly changing business environment, organizational innovation and employee innovative work behavior are the basis for high organizational performance and long-term success. Against this background, the purpose of this study is to propose a conceptual model that links knowledge-based human resource management and intellectual capital and collaborative human resource management practices to innovative behavior at the level of the employee in the organizational context. Drawing on the existing literature and conceptual argument, the present study will argue for this strategic importance of workers’ innovative performance in promoting organizational success. Specifically, the study argues that adopting KHRM and CHRM practices are effective strategies that foster innovation within the organization. Furthermore, the research also reports the critical importance of intellectual capital in driving innovative work behavior. Intellectual capital acts as a mediator between HRM practices and innovative work behavior. The study's theoretical framework and conceptual propositions provide valuable insights into understanding and promoting innovation inside organizations. Additional investigation into these connections would provide pragmatic advice for HRM experts and organizational leaders who want to cultivate creativity and drive achievement within their organizations.

Cloud-edge-end Collaborative Multi-service Resource Management for IoT-based Distribution Grid

Cloud-edge-end Collaborative Multi-service Resource Management for IoT-based Distribution Grid

The Role of Trust in Dependence Networks: A Case Study

In a world where the interconnection and interaction between human and artificial agents are continuously increasing, the dynamics of social bonds and dependence networks play a fundamental role. The core of our investigation revolves around the intricate interplay between dependence and trust within a hybrid society, populated by human and artificial agents. By means of a structural theory, this study offers valuable insights into the utilization of dependence networks and their impact on collaborative dynamics and resource management. Most notably, agents that leverage dependence, even at the cost of interacting with low-trustworthiness partners, achieve superior performance in resource-constrained environments. On the other hand, in contexts where the use of dependence is limited, the role of trust is emphasized. These findings underscore the significance of dependence networks and their practical implications in real-world contexts, offering useful practical implications in areas such as robotics, resource management, and collaboration among human and artificial agents.

Trade-off and driving factors of water-energy-food nexus in Mu Us sandy land, China

Water, energy, and food are essential resources for human survival. The trade-off among these resources can limit regional economic development and pose challenges to livelihood security. In this study, we constructed a root-mean-square deviation (RMSD) index to measure the water-energy-food nexus (WEFN) trade-off. Multi-source data and analysis methods, including ordinary least square (OLS), geographically weighted regression (GWR), and geographic detectors (GD) are employed to explore the trade-offs of water, energy, and food resources and their dominant influencing factors in the Mu Us Sandy Land. The results indicate that the trade-off between regional water-food sub-nexus exhibited the highest magnitude, and trade-offs among all sub-nexus types demonstrated significant fluctuations in spatial distribution. Both OLS and GWR results identified temperature, precipitation, normalized differential vegetation index, slope, digital elevation model, and nighttime light as significant factors influencing the spatial differentiation of WEFN trade-offs. Moreover, geographic detector analysis indicated that precipitation is the dominant factor, and its interaction with other influencing factors further impacts the regional WEFN trade-off. The discussion section examines the RMSD method's effectiveness, the dominant factors shaping WEFN trade-offs, as well as scale effect in WEFN trade-offs in the Mu Us area and underscores that mitigation of water shortages can yield a leverage effect, leading to a marked reduction in the trade-offs. This research contributes new ideas and tools to WEFN research from a geographic perspective, aiming to provide guidance for collaborative resource management and regional sustainable development.

60 NICU Manager’s Perception of High Unit Activity and Its Association with Patient Care in the Neonatal Intensive Care Unit

Abstract Background Each year, 15,000 critically ill neonates require specialized care that is offered in only 32 tertiary neonatal intensive care units (NICUs) in Canada. Previous studies have shown associations between nurse-to-patient ratios and higher bed occupancy with patient outcomes. Objective To explore neonatal managers’ perceptions of organizational factors in Canadian NICUs. Methods This was a web-based cross-sectional survey, consisting of 20 questions pertaining to the determinants of resource allocation, the ascertainment of high occupancy state, as well as the different challenges and mitigation strategies implemented during periods of high unit strain. The survey was designed following the CHERRIES guidelines and was critically reviewed through a two-round validation process prior to deployment. It was sent by e-mail to the unit managers of all Canadian Level-3 NICUs between August and November 2022. They were invited to respond based on their unit’s practices in 2021. The analysis was conducted using descriptive statistics, where units were dichotomized by size (small unit ˂36 beds, large unit ≥36 beds). Results A total of 24 unit managers (75%) completed the survey. Most respondents relied exclusively on clinical judgment to estimate the total number of nurses required per shift (33%) and the individual nurse-to-patient ratios (67%) as opposed to nursing workload assessment tools (8% and 21%, respectively). The response for the proportion of nursing shifts perceived as adequately staffed was: 0% for 100% adequately staffed, 37% for ˃80% adequately staffed, and 27% for 60%-80% adequately staffed. The response for the proportion of nursing shifts perceived as understaffed to the point of being unsafe for patient care was: 17% for no understaffed, 52% for ˂20% understaffed, and 9% for 20%-40% understaffed. The most common organizational challenges were personnel recruitment and unit occupancy (each reported as being a major issue by 38% of respondents). Although most (75%) respondents believed that high occupancy, especially when exceeding 90%, likely increases the risk of adverse patient outcomes, they revealed implementing mitigation strategies to reduce occupancy only when it exceeded 95%. At that point, the most common strategies implemented were to call for additional voluntary nurses, implement voluntary nursing overtime, and accelerate patient transfer. Conclusion Although Canadian NICUs form a very heterogeneous group, managers shared similar perspectives in terms of staffing and occupancy challenges. This emphasizes the need for collaborative NICU person-power resource management practices to improve neonatal intensive care service organization.

A New Evolution Model for Collaborative Management of Knowledge Resources

Collaborative management of knowledge resources is a particularly important issue in the context of collaborative development of vocational education since it can increase utilization of knowledge resources, narrow knowledge gap, promote educational equity, and support the innovation and collaboration of vocational education. However, currently available studies and methods are quite deficient in the aspect of knowledge attribute regulation, such as the difficulty in meeting requirements for different-type knowledge resources, the ignorance of the dynamic feature of knowledge resources, and the insufficient consideration of knowledge attributes. To solve these matters, this study aims to discuss the importance of collaborative management of knowledge resources in a backdrop of the collaborative development of vocational education, with special attention on the key role of knowledge attribute regulation in collaborative management of knowledge resources. In the paper, a collaborative knowledge resource management evolution model was built to reveal the dynamic evolution process of the collaborative management of knowledge resources, so as to provide theoretical evidences for the decision-making of knowledge attribute regulation. On this basis, the paper further proposed a decision-making method of knowledge attribute regulation for the collaborative management of knowledge resources, in the hopes of realizing efficient collaborative management of knowledge resources and promoting the collaborative development of vocational education.

Maximizing team development for open innovation in digital product development: the role of collaborative HRM and relational leadership

PurposeThe purpose of the study was to maximize team members' collaboration and develop relationships in a newly formed team to engage with internal and external partners to achieve open innovation (OI) in product development. The authors examine the role of collaborative human resource management (HRM) and relational leadership (RL) in this process.Design/methodology/approachThe study took a two-stage qualitative methodological approach to examine relational leadership as it emerges in a newly formed cross-functional team at a large German fashion house. In stage one, 10 interviews were conducted with members of the new project team over three months and in stage two six external knowledge exchange partners were interviewed.FindingsCollaborative HRM promotes greater social exchange, trust and commitment of team members internal and external to the organization to support the emergence of RL, which is critical for OI. The authors found that collaborative HRM practices such as team-based recruitment, team-based training, team-based performance management with rewards systems and job design support the emergence of RL. Moreover, RL practices such as congruence and commitment towards team goals subsequently promoted the development of knowledge sourcing and sharing (KSS) to support OI.Originality/valueThe study is the first to demonstrate how collaborative HRM enables RL practices to help newly formed teams overcome challenges with achieving KSS to successfully engage with internal and external partners for OI. The authors contribute to HRM theory development of the relationship between HRM and OI by conceptualizing the OI process as a social construction through collaborative HRM and relational leadership.

An Edge-AI-Enabled Autonomous Connected Ambulance-Route Resource Recommendation Protocol (ACA-R3) for eHealth in Smart Cities

The Autonomous Connected Ambulance (ACA) has been an unprecedented necessity in the demand-supply management sector of the healthcare sector. However, the traditional prototypes designed for such an un-manned vehicle do not match the demands of the advanced communication technologies incorporated in today’s sophisticated distributed networks. As a result, in the current era of edge computing strengthened by many AI-enabled algorithms, there is an urgent need to design a Route Resource Recommendation Protocol for ACA under Edge-AI. Designing such a protocol requires addressing the major challenges to optimize the routes for ACA and, thereby, enhance the services of emergency eHealth centers through a governing telehealth monitoring administrator. Therefore, in this article, a dedicated and novel “ACA-Route Resource Recommendation (R3)” protocol is proposed to address the issues of connectivity and resource management in ACA to optimize the routes for ACA. The ACA-R3 protocol abides by the operational standards of both the eHealth protocol and governing protocol. The primary objective of the current research work was to reduce the handover time and simplify the patient-information exchange during the time of demanded emergencies. The proposed ACA-R3 protocol can enhance the collaborative distributive resource management for reliable decision-making from the data generated by the GPS tracking unit in ACA and Edge-AI. The experimental results obtained from three different cases of traffic congestion are evaluated, validated, and reported in this article.

Collaborative route optimization and resource management strategy for multi-target tracking in airborne radar system

In this paper, a collaborative route optimization and resource management (CRO-RM) strategy is developed for multi-target tracking (MTT) in airborne radar system. The mechanism of the proposed CRO-RM strategy is to collaboratively coordinate the kinematic velocity, heading angle, transmit power, waveform pulselength and bandwidth of the airborne radar, in order to achieve accurate target state estimation while adhering to platform limitations and system resources budget constraints. The analytical expression for the Bayesian Cramér-Rao lower bound (BCRLB) on resources optimization and route planning parameters is derived and utilized as the optimization criterion for the CRO-RM strategy. It is shown that the resulting optimization problem is nonlinear and nonconvex since the above adaptable parameters are coupled in the optimization function and constraints. By exploiting the partition technique and cyclic minimization method, embed with the barrier function method for route planning, the semi-definite programming (SDP) for transmit power allocation and the finite grid search for waveform parameters selection, a partition-based four-step solution method is proposed to tackle the optimization problem effectively. Simulation results demonstrate that the proposed CRO-RM strategy achieves the best MTT performance compared with other benchmarks.

Hierarchical Cross-Domain Satellite Resource Management: An Intelligent Collaboration Perspective

The expansion of satellite applications induces the formation of the multi-domain satellite system (MDSS) containing multiple domains with specific applications such as earth resource remote sensing and the Internet of remote things. Resource management is pivotal in enhancing the scheduling capability of the MDSS. However, this is challenging since the dynamic buffer space and communication opportunity, as well as the uncertain data traffic, exacerbate the difficulty of matching satellite resources with data traffic. Moreover, the coexistence of resource competition and collaboration across domains aggravates the dilemma of cross-domain collaboration. In this paper, we propose a hierarchical cross-domain collaborative resource management framework that can flexibly allocate the mission data through local intra-domain and global cross-domain scheduling. Then, to match the uncertain demands of missions with dynamic and limited resources, we propose a multi-agent reinforcement learning-based resource management method to guide collaboration for multi-satellite data carry-forward in a domain. Further, considering resource competition and collaboration in MDSS, we propose a domain-satellite nested matching game data scheduling algorithm to achieve pair-wise stable collaboration of cross-domain satellites. The simulation results indicate that the proposed algorithm improves the amount of offloaded data by 64.4% and 12.7% compared to the non-collaborative and the non-cross-domain schemes, respectively.

The role of collaborative human resource management in supporting open innovation: A multi-level model

With open innovation (OI) playing an important role in many organizations' innovation strategy, there is growing interest in the human aspects of OI. An important challenge for managing OI remains the motivation of individuals for knowledge sharing and sourcing (KSS). To address this issue, we argue that managers responsible for OI need to use collaborative human resource management (collaborative HRM) practices to create the conditions to develop relational leadership and an open innovation mindset (OI mindset) among employees. Since OI research is largely focused on the organizational level, the micro-foundations of OI, as well as the interdependencies across team and individual levels are not yet fully understood. There is no systematic approach for understanding the role of collaborative HRM and the process through which employees' KSS and use OI within their organizations. We build on social exchange theory to develop a multi-level model of collaborative HRM practices used through relational leadership and OI mindset to enable employees to KSS and improve OI performance.

How CEO transformational leadership impacts organizational and individual innovative behavior: collaborative HRM as mediator

PurposeThe purpose of this research is to examine how the chief exective officers (CEO's) transformational leadership impacts organizational innovation and individual innovative behavior, through the mediating role of collaborative human resource management (HRM) practices.Design/methodology/approachBy using a stratified sampling technique, the data were obtained from ten Microfinance banks located in five metropolitan cities of Pakistan. By adopting a purposive sampling technique, total 427 responses were received out of which 411 responses were considered for data analysis. Structural equation modeling (SEM) technique was adopted using AMOS 21.0 to test the developed hypotheses.FindingsThe findings of the study described a significant and positive relationship between CEO transformational leadership and collaborative HRM. The collaborative HRM has also a positive impact on organizational innovation and individual innovative behavior. Moreover, the results revealed that collaborative HRM fully mediates between CEO transformational leadership and organizational innovation. However, partial mediate between CEO transformational leadership and individual innovative behavior.Practical implicationsThe results of this study can be helpful for bank organizations and policymakers who have extended vision and anticipate the significance of innovation in a fast-changing market. The results of the study also provide the space and scope for prospective researchers and scholars for further research.Originality/valueThere is substantial literature existing on the relationship between CEO transformational leadership, organizational innovation and individual innovative behavior. However, the study finds this that only few researchers took the opportunity to observe the mediating role of collaborative HRM on the relationship between CEO transformational leadership, organizational innovation and individual innovative behavior in the context of Pakistan.

Understanding perspectives on climate hazards, water management, and adaptive transformation in an exurban community

ABSTRACT Climate change and exurban development pose challenges for water resources. This paper examines the perceptions and adaptive responses to those stressors among stakeholders engaging in exurban water management. Drawing on 42 interviews with planners, water managers, and local experts, we analyze perspectives on water-related hazards in the Hood River watershed, Oregon, and identify contrasting approaches to adaptation. Interview subjects identified climate-related hazards as most significant, with relatively less – although not insignificant – concern about development. Interviewees understood the role of the Watershed Group in four different ways: resistance to change, sustaining the present system, adapting to improve resilience, or transformational adaptation. Despite tensions between these approaches, the Watershed Group empowers local actors, offering grounds for social development. This study indicates that exurban areas may be poised to experiment and develop methods of collaborative resource management that reconcile different interests toward transformational adaptations to the dual challenges of climate change and urbanization.

Effects of Cognitive Loading on Pilots and Air Traffic Controller Performance: Implications for Neural Dynamics and Cognitive Flow

The digitized environment in aviation operations has seen marked growth and expansion as new technologies arrive and are implemented. The flight deck and air traffic control functions are two areas where growth is particularly robust. Previous work has identified the effects of compounded cognitive loading and SHELL interfaces in these work environments, and the potential consequences when relief or collaborative resource management is not employed effectively. This paper examines the relationship of cognitive loading in the context of cognitive flow to identify potential areas where neural metrics might aid in a better understanding of the dynamics to determine thresholds of overload. Application of the Triple-Network Model of neural regulation dynamics and Polyvagal Theory are explored for potential relationships to compromised situation awareness and working memory constraints. Conclusions indicate that when cognitive flow is disrupted, cognitive processing loads on working memory expand exponentially and rapidly reach a plateau that inhibits safe performance. Implications suggest a more focused effort in systems and training to address neural metrics and cognitive processing rates.

A methodological framework for modeling sustainability visions: A case study of groundwater management in Faizpur distributary, Pakistan

Over the last two decades, participatory modeling has been advocated as an effective method towards integrated, adaptive, and collaborative water resources management. However, in developing countries, such as Pakistan, its adaptation is limited due to high cost, time, and technical skills of stakeholders. The proposed research aims to develop a stepwise participatory modeling framework with special focus to the problem of groundwater depletion in the Faizpur distributary of Bari Doab basin, Pakistan. Currently, due to absence of a well define groundwater management policy together with low social awareness level, groundwater sustainability is at risk in term of its quality and quantity. For participatory modeling individual causal loop diagrams were developed with potential stakeholders, followed by a merged causal loop diagram to represent a holistic view of the complete system. The final merged diagram helps in understanding different system processes and allows for a more comprehensive qualitative assessment of stakeholder proposed policies. Among other proposed policies (e.g., revise cropping pattern, gray water reuse, and construction of dams) water pricing is found to be the most effective policy. This policy proved to be helpful in improving irrigation efficiency and hence helps in reducing groundwater extraction. The policy may face resistance from upstream farmers due to excessive and cheap availability of irrigation water in their area, but can help in water reallocation and shift traditional methods of farming to more advanced techniques. The results point to social-economic aspects of groundwater management that have not been considered by other modeling studies to date. Moreover, this approach can improve the role of local stakeholder in decision-making processes concerning socio-environmental regulation and climate change mitigation policies.

Spatial Correlation Network of Water Use in the Yangtze River Delta Urban Agglomeration, China

The increasingly severe water crisis is restricting sustainable development of the Yangtze River Delta Urban Agglomeration (YRDUA), highlighting the importance of collaborative water resources management. Based on the dataset from 26 cities between 2001 and 2020 in the YRDUA, this study applies Social Network Analysis to research the spatial correlation of urban water utilization, which helps identify the interaction of water use behaviors among different cities. Results show that the close relationships among urban water consumption in the YRDUA have formed a network, and there is no isolated city in the water use correlation network. During the research period, the water network affinity and network density showed an overall upward trend, meaning the number of actual connections in the network was increasing. However, the network hierarchy first decreased and then raised, and the network efficiency first increased and then decreased, indicating the structural evolution of the water network was gradually detrimental to the effective dissemination of water use information. The urban water consumption correlation network also shows a core-peripheral structure. Shanghai, Wuxi, Suzhou, and Hangzhou have higher coreness and centralities, dominating the water network and controlling major information spillover channels. Therefore, it is essential to focus on the overflow effects of urban water consumption and management among different regions, optimize the structure of the water consumption correlation network, exploit the core cities’ radiation effects, and realize collaborative management for regional water resources.

Prioritizing conservation in sub-Saharan African lakes based on freshwater biodiversity and algal bloom metrics.

As agricultural land use and climate change continue to pose increasing threats to biodiversity in sub-Saharan Africa, efforts are being made to identify areas where trade-offs between future agricultural development and terrestrial biodiversity conservation are expected to be greatest. However, little research so far has focused on freshwaterbiodiversity conservation in the context of agricultural development in sub-Saharan Africa. We aimed to identify lakes and lake areas where freshwater biodiversity is most likely to be affected by eutrophication and Harmful Algal Blooms (i.e., when algae multiply to the extent that they have toxic effects on people and freshwater fauna), some of the most important emerging threats to freshwater ecosystems worldwide, especially with the onset of climate change. Using novel remote-sensing techniques, we identified lakes that demonstrated high biodiversity and algal bloom levels. We calculated the richness of freshwater species and the normalized difference chlorophyll index (NDCI) to prioritize lakes in Ghana, Ethiopia, Zambia, and bordering countries, of high priority for conservation. We identified 169 priority lakes and lake areas for conservation, based on high levels of biodiversity exposed to potentially harmful algal blooms. Zambia had the most lakes identified as conservation priorities (76% of its small lakes and five 100-km2 areas in large lakes). Many of the conservation priority lakes and lake areas identified in this study were in transboundary watersheds; thus, collaborative water resource management and conservation at the watershed scale is needed. The use of remote-sensing tools to prioritize freshwater systems for conservation according to algal-bloom risk is vital in remote, undersampled world regions, especially given the increasing threat posed to freshwater biodiversity by rapidly expanding agriculture and climate change.

Using natural capital and ecosystem services to facilitate participatory environmental decision making: Results from a systematic map

Abstract The rights all people have for involvement in environmental decision making has long been established yet collaborative resource management has had mixed success. Natural capital; the renewable and non‐renewable natural assets that benefit societies, and the flow of ecosystem services these assets provide, are increasingly promoted as approaches that ensure consideration of the environment in decision making. Natural capital and ecosystem services concepts can facilitate participation in decision making by explicitly describing the role of the environment in sustaining society. Increased promotion of these approaches requires consideration on how best to involve stakeholders, those involved and affected by a decision, in the process. We conducted a systematic search to identify where stakeholders have participated in natural capital, ecosystem services and nature’s contributions to people decision making, creating a systematic map of 56 case studies. While many papers discussing stakeholders and these concepts were found, few actively engaged stakeholders in a decision‐making process that used the concepts and therefore were included in the map. Where stakeholders were involved, engagement methods included focus group discussion, stakeholder negotiation and scenario development, as well as ecosystem service ranking and mapping. Ranking for prioritisation of ecosystem services was common, with a bias towards using services with a direct tangible economic benefit; food production and tourism, are both prominent examples. A limited number of case studies performed robust participatory methods evaluations, offering little indication of how best to use natural capital or ecosystem services in participatory approaches. Therefore, the work highlights need for greater evaluation of participatory processes involving natural capital to ensure stakeholder engagement is efficient, productive and useful to all involved. Read the free Plain Language Summary for this article on the Journal blog.

Comprehensive Study On EDGE-Cloud Collaborative Computing for Optimal Task Scheduling

In recent years, Cloud and edge computing have got much attention because of the ever-increasing demands. There are many future technologies and advantages for systems to move towards clouds based on information keep methods. This includes a simple IT substructure and administration, and an effective distant approach from any place in the global with the steady computer network connections and efficient cost that cloud engineering can give. These paradigms impose to process the large amounts of generated data close to the data sources rather than in the cloud. One of the considerations of cloud edge based environment is resource management, which typically revolves around resource allocation, resource provisioning, task scheduling and improve performance. Aiming at the future problem of simulating service requests and optimal task scheduling during the operation of the cloud computing/edge computing environment, the real-time optimization scheduling technology of computing resources is studied, and elastic resource optimization scheduling is realized through data feature (quality) mining analysis, and collaborative resource management. Ensure that the simulation service quality meets the mission requirements and provide support. The main goal of this paper is to provide the better and deeper understanding regarding the scheduling approaches in the Edge-Cloud environment that covers the way in the scheduling approaches.