• The Journal of Industrial Distribution & Business
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• v.9 no.9
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• pp.35-44
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• 2018

Purpose - In endless competition, companies pursue cost reduction and work efficiency. So, entrepreneurs try to increase job intensity, which may lead to job stress and high turnovers because of job burnout. But, Information systems are acknowledged as a work support tool that secures work convenience and the productivity of employees. In this study, we aimed to confirm the effects of information systems in reduing the work overload of employees in a human resource intensive industry. Research design, data and methodology - This is based on the job demands-resources model, conducting an empirical analysis of surveys given to hospital employees working in a human resource intensive industry. Results - The research revealed that information systems reduced the work overload of employees in a human resource intensive industry. Conclusion - This study confirmed the effects of information systems as a job resource based on JD-R theory, and presentation of empirical results indicated that information systems alleviate employee job overload and increases job satisfaction in the medical services industry. In the medical services industry, using electronic health record system decreases in work overload, which results in employees gaining time for self-development and time management, reducing job stress, and leading to job satisfaction.

• Journal of Internet Computing and Services
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• v.24 no.2
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• pp.1-10
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• 2023

The Multi-access Edge Computing (MEC) paradigm equips network edge telecommunication infrastructure with cloud computing resources. It seeks to transform the edge into an IT services platform for hosting resource-intensive and delay-stringent services for mobile users, thereby significantly enhancing perceived service quality of experience. However, erratic user mobility impedes seamless service continuity as well as satisfying delay-stringent service requirements, especially as users roam farther away from the serving MEC resource, which deteriorates quality of experience. This work proposes a deep reinforcement learning based service mobility management approach for ensuring seamless migration of service instances along user mobility. The proposed approach focuses on the problem of selecting the optimal MEC resource to host services for high mobility users, thereby reducing service migration rejection rate and enhancing service availability. Efficacy of the proposed approach is confirmed through simulation experiments, where results show that on average, the proposed scheme reduces service delay by 8%, task computing time by 36%, and migration rejection rate by more than 90%, when comparing to a baseline scheme.

• Environmental and Resource Economics Review
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• v.22 no.3
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• pp.547-580
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• 2013

As energy conservation can be realized through changes in the composition of goods and services consumed, there is a need to assess indirect and total household energy consumption. The Korean household sector was responsible for more than 55% of Korea's total energy consumption in the 1995 to 2010 period. More than 69% of household energy consumption was indirect. Thus, not only direct but also indirect household energy consumption should be the target of energy conservation. Electricity consumption became in 2009 the main source of household energy consumption in Korea. Households consume more and more electricity intensive goods and services, a sign of increasing living standards. Decrease in energy intensities of products consumed by Korean households contributed greatly to reduce the increase in the total household energy consumption. However, switching took place towards more energy intensive products, thus the structure effect was negative. It is necessary to direct consumption and production towards much less energy intensive goods and services as to reduce energy consumption or its growth rate. The Korean government should readjust low energy and electricity prices to cost-reflective prices levels as these low prices are one of the main reasons for the consumption of more energy intensive products. This study differentiates prices of oil products and electricity between households and industries, as to allow more accurate estimation.

• Journal of Communications and Networks
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• v.9 no.4
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• pp.457-465
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• 2007

Native information provider(IP) anycast suffers from routing scalability issues and the lack of stateful communication support. For this reason, we propose architecture for scalable and transparent anycast services(ASTAS), a proxy-based architecture that provides support for stateful anycast communications, while retaining the transparency offered by native anycast. Dynamic resource assignment for each initiated session guarantees that a connection is established with the most suitable target server, based on network and server conditions. Traffic engineering in the overlay can be realized in an effective way due to the dissemination of aggregated state information in the anycast overlay. To minimize the total deployment cost for ASTAS architectures, we propose optimized proxy placement and path finding heuristics based on look-ahead information gathered in network nodes. Contrary to a regular integer linear program(ILP) formulation, these heuristics allow to optimize proxy placement in large networks. A use case on a European reference network illustrates that lower proxy costs enable proxy deployment closer to the end-users, resulting in a reduced network load.

• Journal of Information Processing Systems
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• v.8 no.4
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• pp.555-566
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• 2012

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

• Journal of Korean Academy of Nursing
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• v.41 no.3
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• pp.302-312
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• 2011

Purpose: The purpose of this study was to develop a resource-based relative value scale (RBRVS) and its conversion factor for advanced nursing practices carried out by critical care nurse practitioners (CCNP) in intensive care units. Methods: The methodology was developed by calculating CCNP's RBRVS for 32 advanced nursing services based on CCNP's workload and time spent in the context of national health insurance. A cost analysis was performed to estimate the conversion factor of CCNP's RBRVS. The share of CCNP's contribution to fee-for-service in intensive care units was also analyzed. Results: Calculation of the RBRVS of 32 advanced nursing practices showed a range of points from 100.0 to 1,181.4 and an average of 296.1 points. The relevant conversion factor for advanced nursing practices in CCNP were estimated at 37.3-48.4 won. The contribution rate of CCNP's advanced nursing practices in the relative value scale of the national health insurance was estimated at 0.1-31.3%. Conclusion: Measuring the economic value of advanced nursing services will be a basis for esta-blishing a reimbursement system for CCNP's practices and thus encourage a social demand for advanced nurse practitioners.

• Journal of Korean Library and Information Science Society
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• v.22
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• pp.205-244
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• 1995

Originally the concept of the traditional resource sharing has been discussed in the context of bibliographic materials, and has been labor-intensive and high-cost activities. The technology has had a great impact on such pattern of the resource sharing, and has expanded the limited scope of the traditional concept into the sharing of library information in the levels of local, regional and national systems, and expertise, materials, facilities, equipments and personnels of the library system. While the traditional circulation service as a basic method to share library materials by users can provide the resource to a single person at a time, the electronic resource can be shared, by multi-users at a time anytime anywhere. The maximization of the electronic resource sharing requires that publishing process should be fundamentally changed and articles, books, chapters, speech manuscripts, music scores, maps, sound, and other formats of materials should be prepared in machine readable format. This study examined the positive effects of the technology on the resource sharing, but not investigate the concrete and complex problems as to the cost, guidelines, detailed procedures, design details, and intellectual properties and protection involved in the resource sharing. Some findings extracted from the study can be summarized as follows; (1) ILL will lose its meaning as a method to share the materials if they are all in the electronic format and the phrase 'networked information resource' becomes omnipresent. (2) The technology keeps on changing the concept of resource sharing. Today, the scope of resource sharing not only encompasses the sharing of the primary and secondary materials but also the sharing of the processings(eg. cataloging), expertise, user education, special facilities, and the integrated automated library systems. (3) The sharing of the networked resource will be a method to provide better services for library users in the low cost. (4) The a n.0, pplication of the technology to the resource sharing should be focus on the method which allows an end-users to do the direct access to the needed materials, and to be delivered the primary document as soon as possible.

• Advances in environmental research
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• v.5 no.4
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• pp.237-249
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• 2016

The management of municipal solid waste (MSW) is essential for every community; and, it is currently a major challenge in Nigeria. This paper provides an overview of the current MSW management trends in Nigeria and proposes new sustainable MSW management systems. Across Nigerian cities, MSW management is characterized by inefficient collection and transportation to disposal sites. Collection services do not reach some unplanned areas and slums due to poor street network. Even some planned areas are not reached by collection services. The informal sector contributes to waste collection, resource recovery and recycling; however, their activities are not recognized by the governments. Markets exist for recovered materials but more efforts need to be geared towards intensive recovery of materials and expansion of these markets. Despite the high proportion of putrescible matter in MSW, the only form of treatment commonly used currently is open burning for volume reduction. The high organic fraction presents a great opportunity for composting and anaerobic digestion. Ultimate disposal is currently done in open dumpsites. This needs to be upgraded to engineered landfills that are properly sited and adequately operated by well trained personnel. There is an emerging waste stream of concern, electronic-waste (e-waste), that requires urgent sustainable management as e-waste are currently co-disposed with other waste streams or burnt in the open posing detrimental health impacts.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.

• Electronics and Telecommunications Trends
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• v.38 no.5
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• pp.1-11
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• 2023

The emergence of generative hyperscale artificial intelligence (AI) has enabled new services, such as image-generating AI and conversational AI based on large language models. Such services likely lead to the influx of numerous users, who cannot be handled using conventional AI models. Furthermore, the exponential increase in training data, computations, and high user demand of AI models has led to intensive hardware resource consumption, highlighting the need to develop domain-specific semiconductors for hyperscale AI. In this technical report, we describe development trends in technologies for hyperscale AI processors pursued by domestic and foreign semiconductor companies, such as NVIDIA, Graphcore, Tesla, Google, Meta, SAPEON, FuriosaAI, and Rebellions.