The behavioral and dynamic implications of an ERP implementation/installation are, to say the least, not well understood. Getting the switches set to enable the ERP software to go live is becoming straightforward. The really difficult part is understanding all of the dynamic interactions that accrue as a consequence. Dynamic causal and connectionist models are employed to facilitate an understanding of the dynamics and to enable control of the information-enhanced processes to take place. The connectionist model ran be analyzing (behind the scenes) the information accesses and transfers and coming If some conclusions about strong linkages that are getting established and what the behavioral implications of those new linkages and information accesses we. Ultimately, the connectionist model will come to an understanding of the dynamic, behavioral implications of the larger ERP implementation/installation per se. The underlying connectionist model will determine information transfers and workflow. Once a map of these two infrastructures is determined by the model, it becomes a relatively easy job for an analyst to suggest improvements in both. Connectionist models start with analog object structures and then use learning to produce mechanisms for managerial problem diagnoses. These mechanisms are neural models with multiple-layer structures that support continuous input/output. Based on earlier work performed and published by the author[10][11], a Connectionist ReasOning and LEarning System(CROLES) is developed that mimics the real-world reasoning infrastructure. Coupled with an explanation subsystem, this system can provide explanations as to why a particular reasoning structure behaved the way it did. Such a system operates in the backgmund, observing what is happening as every information access, every information response coming from each and every intelligent node (whether natural or artificial) operating within the ERP infrastructure is recorded and encoded. The CROLES is also able to transfer all workflows and map these onto the decision-making nodes of the organization.
Today's Virtual Reality(VR) technology has become considerably more affordable and sophisticated with rapidly advancing computer technology. Photo-realistic visual representations along with real-time interactivity are now achievable without special programing knowledge and expensive equipment. The purpose of this paper is to establish how simple, credible user testing methods based on VR simulations can be utilized to improve design decision making towards more sensible design solutions for end-users. Results of a case study demonstrate the value of potential user-testing feedback in a simulated 3D virtual service space, i.e., restaurant interior. User feedback from the VR simulation includes preferences in table location, privacy, and values of seating preferred tables. A new design framework incorporating an empirical testing method for service spaces is presented. The overall design process, the development of the VE, and the user testing method and findings are discussed. This study provides useful guidances for future efforts in the areas where such technology may benefit to understand end-user feedbacks in design process.
Bronchodilators provide improvements in lung function and reductions in symptoms and exacerbations, and are the mainstay of pharmacological management of chronic obstructive pulmonary disease (COPD). The Global Initiative for Chronic Obstructive Lung Disease strategy recommends the use of a combination of long-acting ${\beta}_2-agonist$/long-acting muscarinic antagonists (LABA/LAMA) as the first-line treatment option in the majority of symptomatic patients with COPD. This review provides an indirect comparison of available LABA/LAMA fixed-dose combinations (FDCs) through discussion of important efficacy and safety data from the key literature, with the objective of providing physicians with a framework for informed decision-making. LABA/LAMA FDCs provided greater benefits compared with placebo and similar or greater benefits compared with tiotropium and salmeterol/fluticasone in improving lung function, dyspnea, health-related quality of life, reducing rescue medication use and preventing exacerbations, although with some variability in efficacy between individual FDCs; further, tolerability profiles were comparable among LABA/LAMA FDCs. However, there is a disparity in the amount of evidence generated for different LABA/LAMA FDCs. Thus, this review shows that all LABA/LAMA FDCs may not be the same and that care should be taken when extrapolating individual treatment outcomes to the entire drug class. It is important that physicians consider the efficacy gradient that exists among LABA/LAMA FDCs, and factors such as inhaler devices and potential biomarkers, when choosing the optimal bronchodilator treatment for long-term management of patients with COPD.
Background: Elements of informed consent including capacity, disclosure, understanding, voluntariness, and permission of the participant, are all crucial for clinical trials to be legally and ethically valid. During the informed consent process, the patient information leaflet is an important information source which prospective research subjects can utilize in their decision-making. In the adequate provision of information, KGCP guideline necessitate 20 specific items, as well as the use language that individuals can understand. This study measures the vocabulary level of patient information leaflets in an effort to provide an objective evaluation on the readability of such material. Methods: The word difficulty of 13 leaflets was quantitatively evaluated using Kim kwang Hae's vocabulary grading framework, which was compared to the difficulty level of words found in the $6^{th}$ grade Korean textbook. The quantitative outcomes were statistically analyzed using chi-squared tests and linear by linear association for ordinal data. Results: There was a statistically significant difference between the vocabulary level and frequency of words in leaflets and the 6th Korean textbook. The leaflets were on average 260 sentences and about roughly 15 pages long, including lay language (easier or equal to language used in primary school) of around 12% less; technical language of around 4.5% more. As the vocabulary grades increase, there was a distinct difference in vocabulary level between Korean textbook and each information leaflet (p < 0.001). Conclusion: Patient information leaflets may fail to provide appropriate information for self-determination by clinical trial subject through the difficulty level of its wording. Improvements in the degree of patients' understanding and appropriate use of information leaflets are collaboratively equipped to strengthen patient's autonomy and therefore guaranteeing participant's rights.
Context: Genuine community participation does not denote taking part in an action planned by health care professionals in a medical or top-down approach. Further, community participation and health education on breast cancer prevention are not similar to other activities incorporated in primary health care services in Iran. Objective: To propose a model that provides a methodological tool to increase women's participation in the decision making process towards breast cancer prevention. To address this, an evaluation framework was developed that includes a typology of community participation approaches (models) in health, as well as five levels of participation in health programs proposed by Rifkin (1985&1991). Method: This model explains the community participation approaches in breast cancer prevention in Iran. In a 'medical approach', participation occurs in the form of women's adherence to mammography recommendations. As a 'health services approach', women get the benefits of a health project or participate in the available program activities related to breast cancer prevention. The model provides the five levels of participation in health programs along with the 'health services approach' and explains how to implement those levels for women's participation in available breast cancer prevention programs at the local level. Conclusion: It is hoped that a focus on the 'medical approach' (top-down) and the 'health services approach' (top-down) will bring sustainable changes in breast cancer prevention and will consequently produce the 'community development approach' (bottom-up). This could be achieved using a comprehensive approach to breast cancer prevention by combining the individual and community strategies in designing an intervention program for breast cancer prevention.
Korean Journal of Computational Design and Engineering
/
v.21
no.3
/
pp.252-266
/
2016
An architectural pre-design, which is conducted prior to the architecture design, supports fundamental configuration during the entire AEC project by predicting the cost, demand, etc., of the building, and is therefore gaining importance. In particular, the massing calculation of the pre-design phase should be prioritized, as it is fundamental to architectural outline. However, most architects depend on only their experience and intuition while conceptualizing an integrated framework of design conditions, including the building code and requirements for the massing calculation of the object. Therefore, many difficulties arise in terms of performing appropriate tasks. Thus, the purpose of this study is to implement a knowledge-based BIM for explicitly representing the design knowledge, which is the basis of decision making for an architect while performing the massing calculation. In particular, the 3D knowledge relevant to a project can be provided and accumulated in the massing calculation by the BIM system; this facilitates an integral understanding. Consequently, the approximate result of massing calculation in 3D BIM environment, through both the knowledge-based BIM template and plug-in, can be swiftly provided to the architect. In addition, the architect can invent various alternatives, estimate resulting costs, and reuse the accumulated knowledge in future BIM design processes.
Since 1998, government-led Korean venture business boom now proceeds into the stage of market-oriented venture business growth. At such a moment, this research is to clarify the relations between the success factors of Korean venture businesses, based on domestic and foreign documentary surveys and inquiry surveys over domestic venture businesses. This research starts from the necessity to overcome the limits of the existing researches by uni-dimensional probing research into success factors of Korean venture businesses and to manifest the multi-dimensional relations between the success factors from the various viewpoints. And this research adopts System-Dynamics methodology to manifest and utilize relations among those factors, avoiding the existing metrical approaches. This research can be called a new approach to the current ecosystems of Korean venture in which whether venture businesses success in Korea is considered to depend on the list on the stock market. For this, this research implemented verification analysis through the simulations of each factor at various levels to build causality map which clarifies the causality of success factors of venture businesses through the System-Dynamics methodology and to utilize it as a way of supporting tool for decision-making of venture businesses. This research will be able to suggest the reactions depending on various internal and external situations. This research tried to manifest the causality map of each factor on the basis of inquiry surveys and documentary surveys to verify feedback among each factor by the SD methodology. This research will be a basis bolster up the still fragile substructure of venture businesses through an efficient analysis framework using the verification of SD methodology and resulting outcomes from this research.
UN-REDD (United Nations programme on Reducing Emissions from Deforestation and forest Degradation) is currently being emerged as one of important mechanism to reduce carbon dioxide in relation to the deforestation. Although administrative boundary has already gained world-wide recognition as a typical method of monitoring unit in the process of GHG (Greenhouse Gas) reduction project, this approach did not provide a realistic evidence in the carbon sequestering monitoring in terms of UN-REDD; the meaningful comparison of land use patterns among watershed boundaries, interpretation for distribution trends of carbon density, calculation of opportunity cost, leakage management, etc. This research proposes a comparative evaluation framework in a more objective and quantitative way for carbon sequestering monitoring between administrative and watershed boundary approaches. Mt. Geumgang of North Korea was selected as a survey objective and an exhaustive and realistic comparison of carbon sequestration between the two approaches was conducted, based on change detection using TM satellite images. It was possible for drainage boundary approach to identify more detailed area-wide patterns of carbon distribution than traditional administrative one, such as estimations of state and trends, including historical trends, of land use / land cover and carbon density in the Mt. Geumgang. The distinctive changing trends in terms of carbon sequestration were specifically identified over the watershed boundary from 4.0% to 34.8% while less than 1% difference was observed in the administrative boundaries, which were resulting in almost 21-22%. It is anticipated that this research output could be used as a valuable reference to support more scientific and objective decision-making in introducing watershed boundary as carbon sequestering monitoring unit.
The world is in the great change by the digital revolution and is changing fast to knowledge-based society beyond information society. That is to say, Knowledge is the most powerful source of competitiveness. This change of environment needs new paradigm in design. As of today, There is need to study systematically and approach practically about knowledge management. So that, we are intend to reconstruct theoretical system from design-oriented viewpoint and propose knowledge-creation paradigm through the importing technique of knowledge management into design. This study is to develop theoretical frame work of knowledge management in design as a basic study on the implementation of knowledge management for design. By the method of study for this, It was studied literature and precedent. Further more, proposed the appropriate direction of knowledge management implementation in design. Also, It was composed knowledge management system of the infra system, information system, decision-making support system and knowledge competency. Finally, The technical feature of knowledge management system was analyzed from a information technology point of view.
Ramadhani, Adyan Marendra;Kim, Na Rang;Lee, Tai Hun;Ryu, Seung Eui
Journal of Korea Society of Industrial Information Systems
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v.23
no.4
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pp.81-92
/
2018
Bitcoin is a cryptographic digital currency and has been given a significant amount of attention in literature since it was first introduced by Satoshi Nakamoto in 2009. It has become an outstanding digital currency with a current market capitalization of approximately $60 billion. By 2019, it is expected to have over 5 million users. Nowadays, investing in Bitcoin is popular, and along with the advantages and disadvantages of Bitcoin, learning how to forecast is important for investors in their decision-making so that they are able to anticipate problems and earn a profit. However, most investors are reluctant to invest in bitcoin because it often fluctuates and is unpredictable, which may cost a lot of money. In this paper, we focus on solving the Bitcoin forecasting prediction problem based on deep learning structures and neural decomposition. First, we propose a deep learning-based framework for the bitcoin forecasting problem with deep feed forward neural network. Forecasting is a time-dependent data type; thus, to extract the information from the data requires decomposition as the feature extraction technique. Based on the results of the experiment, the use of neural decomposition and deep neural networks allows for accurate predictions of around 89%.
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