Heterogeneous Resources Research Articles

Epistemic tensions in R&D alliances and the role of inter-organizational management controls

Abstract Access to heterogeneous knowledge resources is suggested in the literature as an important explanation of firm innovation and performance. The exchange of knowledge, however, can be a complex managerial challenge, especially between different epistemic communities. Our research focuses on the concept of epistemic communities to illuminate the complexity of tensions that arise in heterogeneous knowledge exchange in alliances, thus filling a gap in the literature. Using the Straussian grounded theory case study approach, our research investigates the emergence of horizontal, vertical, and inter-organizational epistemic tensions and explores management controls as instruments to guide the knowledge exchange in intermediary-driven research and development alliances. We find that the source of multiple epistemic tensions is rooted in the natural social behaviors of epistemic community members and further shows how these behaviors influence the effective use of inter-organizational management controls in facilitating heterogeneous knowledge exchange.

Enhancing Federated Learning in Heterogeneous Internet of Vehicles: A Collaborative Training Approach

The current Internet of Vehicles (IoV) faces significant challenges related to resource heterogeneity, which adversely impacts the convergence speed and accuracy of federated learning models. Existing studies have not adequately addressed the problem of resource-constrained vehicles that slow down the federated learning process particularly under conditions of high mobility. To tackle this issue, we propose a model partition collaborative training mechanism that decomposes training tasks for resource-constrained vehicles while retaining the original data locally. By offloading complex computational tasks to nearby service vehicles, this approach effectively accelerates the slow training speed of resource-limited vehicles. Additionally, we introduce an optimal matching method for collaborative service vehicles. By analyzing common paths and time delays, we match service vehicles with similar routes and superior performance within mobile service vehicle clusters to provide effective collaborative training services. This method maximizes training efficiency and mitigates the negative effects of vehicle mobility on collaborative training. Simulation experiments demonstrate that compared to benchmark methods, our approach reduces the impact of mobility on collaboration, achieving large improvements in the training speed and the convergence time of federated learning.

Multilinguality and LLOD: A survey across linguistic description levels

Limited accessibility to language resources and technologies represents a challenge for the analysis, preservation, and documentation of natural languages other than English. Linguistic Linked (Open) Data (LLOD) holds the promise to ease the creation, linking, and reuse of multilingual linguistic data across distributed and heterogeneous resources. However, individual language resources and technologies accommodate or target different linguistic description levels, e.g., morphology, syntax, phonology, and pragmatics. In this comprehensive survey, the state-of-the-art of multilinguality and LLOD is being represented with a particular focus on linguistic description levels, identifying open challenges and gaps as well as proposing an ideal ecosystem for multilingual LLOD across description levels. This survey seeks to contribute an introductory text for newcomers to the field of multilingual LLOD, uncover gaps and challenges to be tackled by the LLOD community in reference to linguistic description levels, and present a solid basis for a future best practice of multilingual LLOD across description levels.

Trusted reputation system for heterogeneous network resource sharing based on blockchain in IoT

Trusted reputation system for heterogeneous network resource sharing based on blockchain in IoT

Asian Pacific Society of Cardiology Consensus Statements on the Use of Percutaneous Coronary Intervention on Stenosis of the Left Main Coronary Artery

Stenosis of the left main coronary artery (LM stenosis) is associated with significant mortality and morbidity due to the large area of affected myocardium. Evidence on the use of percutaneous coronary intervention (PCI) for the treatment of LM stenosis is growing. However, the role of Western guidelines on patients in the Asian-Pacific region is unclear due to anatomical variations, substantial heterogeneity in healthcare resources and infrastructure, and the lack of consensus on the optimal management of LM stenosis in the Asia-Pacific region. Hence, an Asian Pacific Society of Cardiology (APSC) working group developed consensus recommendations on the use of PCI in the management of LM stenosis in the Asia-Pacific region. The APSC expert panel reviewed and appraised the available evidence using the Grading of Recommendations Assessment, Development, and Evaluation system. Consensus recommendations were developed and put to an online vote. Consensus was reached when 80% of votes for a given recommendation were in support of ‘agree’ or ‘neutral’. The resulting 12 statements provide guidance for clinical practitioners in the region on the use of PCI in the treatment of patients with LM stenosis.

A Truthful Randomized Mechanism for Heterogeneous Resource Allocation With Multi-Minded in Mobile Edge Computing

A Truthful Randomized Mechanism for Heterogeneous Resource Allocation With Multi-Minded in Mobile Edge Computing

Dynamic pricing‐based integration for non‐cooperative ubiquitous sensing and communication network

AbstractAs 5G and early‐stage network technologies mature, a wealth of data and experience is accumulated. The ubiquitous sensing and communication network (USCN) can leverage these existing communication infrastructures to maximize resource utilization and reduce the costs of redeployment through integration with modern 6G and beyond 6G networks. However, implementing future integrated sensing and communication (ISAC) functions in USCN may possess different hardware capabilities and communication protocols, presenting significant challenges. This is due to the lack of a unified framework for managing and optimizing these heterogeneous resources, as well as the absence of reasonable performance metrics. This letter proposes a dynamic pricing coordinated resource allocation (DPCRA) mechanism to realize the emerging ISAC in legacy USCNs. The DPCRA is designed to optimize the spectrum efficiency (SE) of USCN and manages resource interactions between heterogeneous devices through price variables, thereby resolving resource conflicts and deadlocks. The novelties include: (1) employing a price mechanism to foster resource cooperation in non‐cooperative networks; (2) introducing an SE metric incorporating an information similarity factor for optimization; (3) dynamically selecting resources to enhance SE. Simulations demonstrate that the DPCRA effectively resolves device deadlock while maintaining high SE levels, promoting device collaboration.

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

Efficient deep reinforcement learning based task scheduler in multi cloud environment

Task scheduling problem (TSP) is huge challenge in cloud computing paradigm as number of tasks comes to cloud application platform vary from time to time and all the tasks consists of variable length, runtime capacities. All these tasks may generated from various heterogeneous resources which comes onto cloud console directly effects the performance of cloud paradigm with increase in makespan, energy consumption, resource costs. Traditional task scheduling algorithms cannot handle these type of complex workloads in cloud paradigm. Many authors developed Task Scheduling algorithms by using metaheuristic techniques, hybrid approaches but all these algorithms give near optimal solutions but still TSP is a highly challenging and dynamic scenario as it resembles NP hard problem. Therefore, to tackle the TSP in cloud computing paradigm and schedule the tasks in an effective way in cloud paradigm, we formulated Adaptive Task scheduler which segments all the tasks comes to cloud console as sub tasks and fed these to the scheduler which is modeled by Improved Asynchronous Advantage Actor Critic Algorithm(IA3C) to generate schedules. This scheduling process is carried out in two stages. In first stage, all incoming tasks are segmented as sub tasks. After segmentation, all these sub tasks according to their size, execution time, communication time are grouped together and fed to the (ATSIA3C) scheduler. In the second stage, it checks for the above said constraints and disperse them onto the corresponding suitable processing capacity VMs resided in datacenters. Proposed ATSIA3C is simulated on Cloudsim. Extensive simulations are conducted using both fabricated worklogs and as well as realtime supercomputing worklogs. Our proposed mechanism evaluated over baseline algorithms i.e. RATS-HM, AINN-BPSO, MOABCQ. From results it is evident that our proposed ATSIA3C outperforms existing task schedulers by improving makespan by 70.49%. Resource cost is improved by 77.42%. Energy Consumption is improved over compared algorithms 74.24% in multi cloud environment by proposed ATSIA3C.

DRL-assisted task offloading in enhanced time-expanded graph (eTEG)-modeled aerial computing

Space–air–ground integrated networks (SAGINs), categorized under aerial computing (AC), are emerging as a promising hierarchical platform designed to meet the seamless connectivity demands of the forthcoming 6G era. However, efficiently offloading ground tasks to space entities via SAGINs presents unprecedented challenges, primarily due to the mobility of these networks. In response, an enhanced time-expanded graph (eTEG) is proposed to model the dynamic distribution of heterogeneous SAGIN resources, including transmission bandwidth, computation, and storage, thereby optimizing task offloading and resource allocation by employing eTEG. Specifically, this optimization challenge is addressed using a deep reinforcement learning (DRL) approach, aimed at streamlining decision-making for task offloading and resource management to significantly reduce end-to-end delay and enhance network performance. Simulation experiments conducted to evaluate the proposed DRL-based method demonstrate its effectiveness in reducing energy consumption and improving stability, thereby outperforming other methods by achieving reduced delays and satisfying user requirements.

Process and mechanism of termite impact on soil and plant.

Termites, as a kind of nesting social insects, are often confused as worldwide "pests" because some of their groups have great destructive effects. The vast majority of termites can regulate ecosystem functions and ser-vices by participating in biogeochemical cycles, known as "ecosystem engineers". We reviewed studies on the effects of termites on the physical, chemical and biological characteristics of mound soil ecosystems and the composition and diversity of plant communities. Termites could form unique soil "biogenic aggregates" and "resource heterogeneity patches", which affect microbial community structure, extracellular enzyme activity, physicochemical property and greenhouse gas emission, thereby affecting plant growth, community composition and structure, and vegetation productivity. However, this effect significantly differed among termite groups and functional groups, and was dependent on regional soil environment and microclimate conditions. Meanwhile, termite-mound could effectively improve ecosystem adaptation or resistance to environmental stress through the above process. Future research should focus on the following directions: 1) studying the trophic cascading effect of termite-centered soil multilevel biological network and the potential effect on biogeochemical cycle from microscale (aggregate level) to macroscale (landscape level); 2) exploring the potential of termite mound soil as a fertility amendment in tropical regions, and mining beneficial microbial functional genes to develop related products for termite control.

Managing the Supply–Demand Mismatches and Potential Flows of Ecosystem Services in Jilin Province, China, from a Regional Integration Perspective

Regional integration strategically reorganizes spatially heterogeneous resources to maximize the overall benefits. Ecosystem services (ESs) are promising targets for regional integration due to their inherent heterogeneity and mobility, yet research in this area remains limited. This study quantifies crop production (CP), water yield (WY), carbon storage (CS), and habitat quality (HQ) for the years 2000, 2010, and 2020 using the InVEST model and identifies four ES bundles through a K-means cluster analysis. A conceptual ecosystem service flow (ESF) network at the service cluster scale is constructed based on county-level ESF data. The results reveal the following: (1) there is an upward trend in the ES budget for all services from 2000 to 2020, coupled with spatial mismatches between supply and demand; (2) deficit nodes for CP and CS services are concentrated in densely populated districts, while deficits in WY and HQ services are mainly in western Jilin Province; (3) Bundles I and II act as “sources” of ES, Bundle IV serves as a “sink”, and Bundle III is the only cluster with a CP surplus, balancing CP services across the province. In addition, this study provides ecological perspectives for understanding regional integration by suggesting differentiated integrated management for different ecosystem bundles.

The Uncertain Value of Cosmopolitan Capital: Teachers at International Schools in Switzerland

Drawing on field theory, this article analyses the trajectories of schoolteachers employed in Swiss international schools, with a focus on the accumulation and mobilization of various forms of capital – and the limits thereof. The heterogeneity of teacher trajectories and resources when entering the international school sector is documented and, through the concept of cosmopolitan capital, light is shed on specific logics of distinction contributing to the segmentation of the teaching sector and the creation of an educational enclave of international schools.

Визуальная интерпретация парадигмы управления организационной системой c разнородными производственными ресурсами

The object of the study is developing a formalized mechanism for managing an organizational system that implements complex production activities, including different types of industrial production, and applies the corresponding heterogeneous resources. The subject of the article is an important intermediate stage of this development, which is an abstract interpretation of the optimal management paradigm of limited resources of such an organizational system, proposed earlier by the authors. The interpretation will allow reasonably forming a strict mathematical model of objects and control processes. The management mechanism in any subject area, providing for adopting management decisions by people authorized to do so, must have an ergonomic development and appropriate information support for these decisions. The so-called result information plays the role of such support, that is the information on the characteristics of the current and planned states of the management object in the problem being solved which is the productivity of the organizational system resources. It seems advisable to carry out such ergonomic and information development of the designed problem-oriented management mechanism in the form of a visual interpretation of its substantive concept. The prototype of such interpretation tools is graphic images used in modelling and designing technical systems, developed in this work in the field of management in organizational systems with heterogeneous resources. The article provides examples of their application, demonstrating the possibility of visual representation of the current state of the organizational system and the set of its possible controlled states.

The credibility assessment of Twitter/X users based organization objectives by heterogeneous resources in big data life cycle

Social network data, such as Twitter/X, is of Big Social Data type. Big social data describes people's social behaviors and interactions. They have high business value for decision-making in organizations. However, because of the anonymous nature of social network users, their credibility is ambiguous. Credibility expresses the accuracy and value of big social data. Despite extensive research on the credibility of big social data, most methods have not paid sufficient attention to the important dimensions of their assessment, including user expertise based on topic, selecting social network features, and labeling them. Furthermore, these methods cannot manage the time, high volume, and speed of big social data. To address these issues, this paper presents a novel model for assessing the credibility of Twitter/X users by integrating Twitter/X with Google Scholar. The model automatically defines users' credibility labels using Google Scholar. Machine learning feature selection methods also select features that affect the credibility of Twitter/X users based on the topic. This study uses Google Scholar and the BerTopic algorithm for effective topic modeling on Twitter/X. The model considers unrelated data management, dynamic user credibility, and organizing activities based on the Big Data lifecycle. Finally, using Linear Regression, Support Vector Regression, K-Nearest Neighbor, Random Forest, Classification and Regression Trees algorithms, the model predicts the credibility of Twitter/X users and proves that it performed better than similar models through Classification and Regression Trees. In addition, the model is generalizable for all organizational purposes due to the integration of heterogeneous resources and feature selection methods.

Agent-based evolutionary game dynamics uncover the dual role of resource heterogeneity in the evolution of cooperation

Cooperation is a cornerstone of social harmony and group success. Environmental feedbacks that provide information about resource availability play a crucial role in encouraging cooperation. Previous work indicates that the impact of resource heterogeneity on cooperation depends on the incentive to act in self-interest presented by a situation, demonstrating its potential to both hinder and facilitate cooperation. However, little is known about the underlying evolutionary drivers behind this phenomenon. Leveraging agent-based modeling and game theory, we explore how differences in resource availability across environments influence the evolution of cooperation. Our results show that resource variation hinders cooperation when resources are slowly replenished but supports cooperation when resources are more readily available. Furthermore, simulations in different scenarios suggest that discerning the rate of natural selection acts on strategies under distinct evolutionary dynamics is instrumental in elucidating the intricate nexus between resource variability and cooperation. When evolutionary forces are strong, resource heterogeneity tends to work against cooperation, yet relaxed selection conditions enable it to facilitate cooperation. Inspired by these findings, we also propose a potential application in improving the performance of artificial intelligence systems through policy optimization in multi-agent reinforcement learning. These explorations promise a novel perspective in understanding the evolution of social organisms and the impact of different interactions on the function of natural systems.

Fruit resources shape sexual selection processes in a lek mating system.

The degree to which within-population variation in sexual trait expression relates to resource heterogeneity remains poorly explored. This is particularly true in lek-mating species, where genetic explanations for male phenotypic variance and mating success are dominant. Here, we demonstrate a link between fine-scale fruit resource availability and indices of male mating success in the white-bearded manakin (Manacus manacus), a lek-mating frugivorous bird that produces energetically costly courtship displays. We used motion-activated camera traps to monitor male display behaviour and female visitation at male courts while concurrently conducting twice-monthly fruit surveys around courts. We observed significant variability in ripe fruit biomass among display courts and leks, and mean fruit biomass at courts significantly predicted male display rates. In turn, male display rate was the strongest predictor of female visitation to courts. Causal modelling supported the hypothesis that hyper-local fruit availability indirectly affects female visitation via its direct effects on male display rate. The demonstration that resource availability at fine spatial scales predicts display rate in a lekking organism-for which resource-related variables are typically not considered to play important roles in shaping male reproductive variance-has implications for the expression, honesty and maintenance of sexually selected traits under fluctuating ecological conditions.

Resource prioritization and allocation in fog computing using hybrid optimization

With the rapid proliferation of IoT devices, the volume of data generated has reached unprecedented levels, necessitating efficient management strategies. Fog computing, complemented by 5G technologies, offers promising solutions to reduce service latency and enhance Quality of Service (QoS). However, allocating resources effectively remains challenging due to factors such as uncertainty, mobility, heterogeneity, and limited resources in fog computing environments. Traditional resource allocation (RA) algorithms often fall short of addressing these complexities. This study proposes a novel approach to RA in fog computing, utilizing a non-linear function to optimize resource allocation. An objective function is introduced, incorporating multi constraints such as resource utilization, service response rate, makespan, migration cost, and communication cost. The methodology emphasizes efficient resource allocation in crucial scenarios, facilitating rapid resource distribution where needed. The novel Coati Integrated Beluga Whale Optimization (CI-BWO) strategy is proposed to achieve optimal resource allocation in fog computing environments. By leveraging CI-BWO, this research aims to overcome the limitations of traditional RA methods and enhance the performance and scalability of fog computing applications. Finally, the superiority of the suggested strategy is assessed by comparison with many existing methods. When the task count is 200, the developed CI-BWO attained less migration cost of around 1.287, while existing models have acquired higher migration costs.

Optimizing scientific workflow scheduling in cloud computing: a multi-level approach using whale optimization algorithm

Cloud computing has evolved into an indispensable tool for facilitating scientific research due to its ability to efficiently distribute and process workloads in a virtual environment. Scientific tasks that involve complicated task dependencies and user-defined constraints related to quality of service (QoS) and time constraints require the efficient use of cloud resources. Planning these scientific workflow tasks represents an NP-complete problem, prompting researchers to explore various solutions, including conventional planners and evolutionary optimization algorithms. In this study, we present a novel, multistage algorithm specifically designed to schedule scientific workflows in cloud computing contexts. This approach addresses the challenges of efficiently mapping complex workflows onto distributed cloud resources while considering factors like resource heterogeneity, dynamic workloads, and stringent performance requirements. The algorithm uses the whale optimization algorithm (WOA) with a two-phase approach to shorten execution time, minimize financial costs, and effectively maintain load balancing.

Dynamic Research on the Collaborative Governance in Urban and Rural Black-Odorous Water: A Tripartite Stochastic Evolutionary Game Perspective

The issue of black-odorous water (BOW) represents a formidable challenge to the current aquatic ecosystems, and its governance exhibits characteristics of low efficiency, susceptibility to relapse, and fragmented management under the Central Environmental Protection Inspection, thereby emerging as a dynamically complex issue in the ecological governance of urban and rural settings. This study introduces Gaussian white noise to simulate environmental uncertainty and design a stochastic evolutionary game model encompassing the central government, local governments, and societal forces based on evolutionary game theory and classical governance theories and concepts. Numerical simulations are conducted to explore trajectories of the strategic evolution of various subjects influenced by numerous factors. Results indicate that under the environment of random disturbances, the strategies of the game subjects show significant fluctuations, but actively cultivating the subject’s initial willingness facilitates collaboration governance in inspection. Moreover, joint construction of a “belief system” by multi-subjects, the intensity of inspection interventions, the integration of heterogeneous resources, and effective punitive measures all influence the governance of BOW, but the efficiency of resource allocation should be considered throughout the governance process. Recommendations are made finally for collaborative governance of urban and rural BOW, promoting the sustainable development of the ecological environment.