• Structural Engineering and Mechanics
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• v.78 no.3
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• pp.351-368
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• 2021

This paper, by applying a reliability-based framework, develops seismic vulnerability macrozonation maps for Tehran, the capital and one of the most earthquake-vulnerable city of Iran. Seismic performance assessment of 3-, 4- and 5-story steel moment resisting frames (SMRFs), designed according to ASCE/SEI 41-17 and Iranian Code of Practice for Seismic Resistant Design of Buildings (2800 Standard), is investigated in terms of overall maximum inter-story drift ratio (MIDR) and unit repair cost ratio which is hereafter known as "damage ratio". To this end, Tehran city is first meshed into a network of 66 points to numerically locate low- to mid-rise SMRFs. Active faults around Tehran are next modeled explicitly. Two different combination of faults, based on available seismological data, are then developed to explore the impact of choosing a proper seismic scenario. In addition, soil effect is exclusively addressed. After building analytical models, reliability methods in combination with structure-specific probabilistic models are applied to predict demand and damage ratio of structures in a cost-effective paradigm. Due to capability of proposed methodology incorporating both aleatory and epistemic uncertainties explicitly, this framework which is centered on the regional demand and damage ratio estimation via structure-specific characteristics can efficiently pave the way for decision makers to find the most vulnerable area in a regional scale. This technical basis can also be adapted to any other structures which the demand and/or damage ratio prediction models are developed.

• Journal of the Korean Society of Systems Engineering
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• v.18 no.2
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• pp.75-93
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• 2022

Machine learning (ML) data-driven meta-model is proposed as a surrogate model to reduce the excessive computational cost of the physics-based model and facilitate the real-time prediction of a nuclear power plant's transient response. To forecast the transient response three machine learning (ML) meta-models based on recurrent neural networks (RNNs); specifically, Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and a sequence combination of Convolutional Neural Network (CNN) and LSTM are developed. The chosen accident scenario is a control element assembly withdrawal at power concurrent with the Loss Of Offsite Power (LOOP). The transient response was obtained using the best estimate thermal hydraulics code, MARS-KS, and cross-validated against the Design and control document (DCD). DAKOTA software is loosely coupled with MARS-KS code via a python interface to perform the Best Estimate Plus Uncertainty Quantification (BEPU) analysis and generate a time series database of the system response to train, test and validate the ML meta-models. Key uncertain parameters identified as required by the CASU methodology were propagated using the non-parametric Monte-Carlo (MC) random propagation and Latin Hypercube Sampling technique until a statistically significant database (181 samples) as required by Wilk's fifth order is achieved with 95% probability and 95% confidence level. The three ML RNN models were built and optimized with the help of the Talos tool and demonstrated excellent performance in forecasting the most probable NPP transient response. This research was guided by the Systems Engineering (SE) approach for the systematic and efficient planning and execution of the research.

• Journal of Korea Society of Industrial Information Systems
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• v.14 no.4
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• pp.37-48
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• 2009

Household service robots should be able to provide their users with a variety of temporal reasoning services. In this paper, we propose an effective way of developing such temporal reasoning services using a domain-independent AI planner. Developing temporal reasoning services with a domain-independent AI planner, we have to address both the knowledge engineering problem of how to represent various real-world temporal constraints in a planning domain definition language, and the system design problem of how to realize the interface between the AI planner and the service consumer. In this paper, we introduce an example scenario and a set of typical temporal constraints for a household service robot, and then present how to represent them in the standard planning domain definition language. We also explain how to implement a service agent based on an AI planner in order to develop and provide new services efficiently.

• Women's Health Nursing
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• v.28 no.4
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• pp.317-328
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• 2022

Purpose: This study aimed to develop an emotive role-play program for nursing students focusing on high-risk pregnancy and analyze its effects on communication skills, clinical performance, and emotional intelligence. Methods: A quasi-experimental nonequivalent comparison group design was adopted with 83 nursing students (experimental group, 45; comparison group, 38) who participated voluntarily in an extracurricular program. The preliminary survey was conducted on November 3 and November 4, 2020, and the follow-up survey was conducted on November 12, 2020, for the comparison group and on November 27, 2020, for the experimental group. A program that included five role-play scenarios related to induced labor, preeclampsia, premature rupture of membranes, preterm labor, and infertility was developed by a group of experts and presented to the experimental group over 11 total hours across 3 days. Each student participated in a role-play scenario as a patient, family member, or nurse and observed three other scenarios. The comparison group received a workbook after the follow-up evaluation. The independent t-test was performed to analyze changes in communication skills, clinical performance, and emotional intelligence. Results: Communication skills (t=1.84, p=.035) and clinical performance (t=2.75, p=.004) significantly increased in the experimental group compared to the comparison group. A significant difference was not observed between the experimental and comparison groups for emotional intelligence (t=1.36, p=.088). Conclusion: The emotive role-play program concerning high-risk pregnancy was effective in improving nursing students' communication skills and clinical performance and can be used in nursing education related to high-risk pregnancy and childbirth.

• Nuclear Engineering and Technology
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• v.55 no.1
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• pp.169-179
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• 2023

Filtered containment system is a passive safety system that controls the over-pressurization of containment in case of a design-based accidents by venting high pressure gaseous mixture, consisting of air, steam and radioactive particulate and gases like iodine, via a scrubbing system. An indigenous lab scale facility was developed for research on iodine removal by venturi scrubber by simulating the accidental scenario. A mixture of 0.2 % sodium thiosulphate and 0.5 % sodium hydroxide, was used in scrubbing column. A modified mathematical model was presented for iodine removal in venturi scrubber. Improvement in model was made by addition of important parameters like jet penetration length, bubble rise velocity and gas holdup which were not considered previously. Experiments were performed by varying hydrodynamic parameters like liquid level height and gas flow rates to see their effect on removal efficiency of iodine. Gas holdup was also measured for various liquid level heights and gas flowrates. Removal efficiency increased with increase in liquid level height and gas flowrate up to an optimum point beyond that efficiency was decreased. Experimental results of removal efficiency were compared with the predicted results, and they were found to be in good agreement. Maximum removal efficiency of 99.8% was obtained.

• Asia Marketing Journal
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• v.6 no.2
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• pp.3-22
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• 2004

Prior researches related to private brands have mainly focused on the competition between private brands and national brands. But private brands can be allied with national brands to overcome its weak points such as low perceived quality. The purpose of this study is to investigate the consumer responses to the alliance between private brands and national brands. Especially this paper focused on the situation in which national brands are used as ingredient brands to improve the consumers' preference toward private brands. We hypothesized that using national brands as ingredient brands would improved the consumers' perceived quality and buying intention toward private brands. Also we supposed that when national brands had moderate congruity with product category, consumers' preference would be most positive. And this study examined the moderating effects of functional risk. Factorial experimental design and scenario method were employed to test the hypotheses. Also we classified the products as utilitarian-hedonic products and compared the effects of ingredient brands on utilitarian products with hedonic products. Empirical results show that when private brands use national brands as ingredient brands, consumer responses become positive. Also when ingredient brands have moderate congruity with products, consumer responses are most positive. But moderate congruity effect is significant only when functional risk is low.

• Steel and Composite Structures
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• v.49 no.5
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• pp.533-546
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• 2023

Cost-effective solutions provided by composite construction are gaining popularity which, in turn, promotes the appearance on the market of new types of composite sections that allow not only to take advantage of the synergy of steel and concrete working together at room temperature, but also to improve their behaviour at high temperatures. When combined with high performance materials, significant load-bearing capacities can be achieved even with reduced cross-sectional dimensions. Steel-reinforced concrete-filled steel tubular (SR-CFST) columns are one of these innovative composite sections, where an open steel profile is embedded into a CFST section. Besides the renowned benefits of these typologies at room temperature, the fire protection offered by the surrounding concrete to the inner steel profile, gives them an enhanced fire performance which delays its loss of mechanical capacity in a fire scenario. The experimental evidence on the fire behaviour of SR-CFST columns is still scarce, particularly when combined with high performance materials. However, it is being much needed for the development of specific design provisions that consider the use of the inner steel profile in CFST columns. In this work, a new experimental program on the thermo-mechanical behaviour of SR-CFST columns is presented to extend the available experimental database. Ten SR-CFST stub columns, with circular and square geometries, combining high strength steel and concrete were tested. It was seen that the circular specimens reached higher failure times than the square columns, with the failure time increasing both when high strength steel was used at the embedded steel profile and high strength concrete was used as infill. Finally, different proposals for the reduction coefficients of high performance materials were assessed in the prediction of the cross-sectional fire resistance of the SR-CFST columns.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.25-41
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• 2024

For the scenario of open pit mining, at present, manual periodic verification is mainly carried out in China with the help of video surveillance, which requires continuous investment in labor cost and has poor timeliness. In order to solve this difficult problem of early warning and monitoring, this paper researches a spatialized algorithmic model and designs an early warning system for open-pit mine transboundary mining, which is realized by calculating the coordinate information of the mining and extracting equipments and comparing it with the layer coordinates of the approval range of the mines in real time, so as to realize the determination of the transboundary mining behavior of the mines. By taking the Pingxiang area of Jiangxi Province as the research object, after the field experiment, it shows that the system runs stably and reliably, and verifies that the target tracking accuracy of the system is high, which can effectively improve the early warning capability of the open-pit mines' overstepping the boundary, improve the timeliness and accuracy of mine supervision, and reduce the supervision cost.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.11-19
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• 2024

Multi-agent systems can be utilized in various real-world cooperative environments such as battlefield engagements and unmanned transport vehicles. In the context of battlefield engagements, where dense reward design faces challenges due to limited domain knowledge, it is crucial to consider situations that are learned through explicit sparse rewards. This paper explores the collaborative potential among allied agents in a battlefield scenario. Utilizing the Multi-Robot Warehouse Environment(RWARE) as a sparse reward environment, we define analogous problems and establish evaluation criteria. Constructing a learning environment with the QMIX algorithm from the reinforcement learning library Ray RLlib, we enhance the Agent Network of QMIX and integrate Random Network Distillation(RND). This enables the extraction of patterns and temporal features from partial observations of agents, confirming the potential for improving the acquisition of sparse reward experiences through intrinsic rewards.

• International Journal of Knowledge Content Development & Technology
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• v.14 no.1
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• pp.19-31
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• 2024

In order to support the mission of the institution in which they are attached, academic libraries provide services in both manual and digital but COVID -19 pandemic that spanned between March and September, 2020 has changed the scenario. With particular reference to Nigeria, about 249,606 cases were confirmed and in order to curb the scourge of this deadly disease, physical academic activities were prevented by Nigeria Centre for Disease Control (NCDC). With this development, innovative tools became indispensable tools for successful delivery of library services in Nigerian academic libraries. Whether or not these tools are still in use for reformation of library service during post- Covid era remains unclear, hence, need for this study. This study examined librarians' use of innovative tools for information dissemination in Nigerian academic libraries during the post-Covid era using a descriptive survey design. Data were obtained both in quantitative and qualitative formats from one hundred and forty-four librarians as respondents. A total enumeration sampling technique was adopted because the population was minimal. Findings of the study revealed that innovative tools such as videoconferencing, WhatsApp, teleconferencing, Facebook, LinkedIn, and web-based learning applications are still in use by librarians for the dissemination of information during the post-Covid era. These tools are useful and beneficial to librarians during the post-COVID era, as they facilitate easy participation and engagement of library users in various discussions. Inadequate funding and lack of advanced technology skills were also identified as major impediments to the successful use of innovative tools for information dissemination. As a result, it was suggested that academic libraries throughout Nigeria prioritize staff training on the necessary digital skills needed to cope in this advanced technology era.