• Advances in nano research
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• v.12 no.5
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• pp.489-499
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• 2022

Nanofluids have recently triggered a substantial scientific interest as cooling media. However, their stability is challenging for successful engagement in industrial applications. Different factors, including temperature, nanoparticles and base fluids characteristics, pH, ultrasonic power and frequency, agitation time, and surfactant type and concentration, determine the nanofluid stability regime. Indeed, it is often too complicated and even impossible to accurately find the conditions resulting in a stabilized nanofluid. Furthermore, there are no empirical, semi-empirical, and even intelligent scenarios for anticipating the stability of nanofluids. Therefore, this study introduces a straightforward and reliable intelligent classifier for discriminating among the stability regimes of alumina-water nanofluids based on the Zeta potential margins. In this regard, various intelligent classifiers (i.e., deep learning and multilayer perceptron neural network, decision tree, GoogleNet, and multi-output least squares support vector regression) have been designed, and their classification accuracy was compared. This comparison approved that the multilayer perceptron neural network (MLPNN) with the SoftMax activation function trained by the Bayesian regularization algorithm is the best classifier for the considered task. This intelligent classifier accurately detects the stability regimes of more than 90% of 345 different nanofluid samples. The overall classification accuracy and misclassification percent of 90.1% and 9.9% have been achieved by this model. This research is the first try toward anticipting the stability of water-alumin nanofluids from some easily measured independent variables.

• Asia Marketing Journal
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• v.20 no.2
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• pp.65-83
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• 2018

Technological advances are bringing autonomous vehicles to the ever-evolving transportation system. Anticipating adoption of these technologies by users is essential to vehicle manufacturers for making more precise production and marketing strategies. The research investigates regulatory focus and consumer innovativeness with consumers' adoption of autonomous vehicles (AVs) and to consumers' subsequent willingness to pay for AVs. An online questionnaire was fielded to confirm predictions, and regression analysis was conducted to verify the model's validity. The results show that a promotion focus does not have a significantly positive effect on the automation level at which consumers will adopt AVs, but a prevention focus has a significantly positive effect on conditional AV adoption. Consumer innovativeness, consumers' novelty-seeking have a significantly positive relationship with high and full AV adoption, and consumers' independent decision-making has a significantly positive effect on full AV adoption. The higher the level of automation at which a consumer adopts AVs, the higher the willingness to pay for them. Finally, using a neural network and decision tree analyses, we show methods with which to describe three categories for potential adopters of AVs.

• Journal of the Korea Safety Management & Science
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• v.25 no.2
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• pp.29-38
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• 2023

Social welfare facilities are used by a wide range of local residents, including vulnerable populations such as the elderly, children, and people with disabilities. During emergencies like fires, confusion can arise as these individuals try to evacuate. Evacuation simulation results have shown that utilizing evacuation systems based on specific evacuation scenarios can significantly decrease the time required for evacuation compared to general evacuation procedures. By anticipating potential fires based on changes in social and facility environments, appropriate evacuation scenarios can be developed and applied to evacuation systems, thus contributing to the safety and security of individuals during emergencies. In conclusion, for social welfare facilities that serve a large number of people, it is necessary to expand the focus on performance-based design depending on the size of the facility, and to continuously develop and train for appropriate evacuation scenarios that align with changing facility environments.

• Journal of Korea Society of Digital Industry and Information Management
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• v.19 no.4
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• pp.97-115
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• 2023

In South Korea, chronic kidney disease(CKD) impacts around 4.6 million adults, leading to a high reliance on hemodialysis. For effective dialysis, vascular access is crucial, with decisions about vascular surgeries often made during dialysis sessions. Anticipating these needs could improve dialysis quality and patient comfort. This study investigates the use of Artificial Intelligence(AI) to predict the timing of surgeries for dialysis vessels, an area not extensively researched. We've developed an AI algorithm using predictive maintenance methods, transitioning from machine learning to a more advanced deep learning approach with Long Short-Term Memory(LSTM) models. The algorithm processes variables such as venous pressure, blood flow, and patient age, demonstrating high effectiveness with metrics exceeding 0.91. By shortening the data collection intervals, a more refined model can be obtained. Implementing this AI in clinical practice could notably enhance patient experience and the quality of medical services in dialysis, marking a significant advancement in the treatment of CKD.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.4_1
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• pp.769-780
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• 2024

For an organization to survive and prosper, it is essential to continuously develop innovative products by proactively anticipating consumers' implicit requirements. The Kano model has become more useful since Sireli et al. (2007) and Tontini (2007) introduced a simple equation for determining the importance of characteristics by using the concept of Kano's Potential Customer Satisfaction Coefficient (PCSC). However, although several studies have utilized the PCSC concept to determine the importance of characteristics, it is surprising that the two equations have been accepted without any validation process. This study aims to propose a modified equation using PCSC and to conduct a validity test of the proposed equation, demonstrating its superiority over the previously suggested two equations, The author analysed 26 Kano related articles (27 cases), and the correlation coefficients were compared with those obtained from direct rating importance, which served as a comparative criterion. The results indicate that the proposed equation is valid for assessing characteristic importance and demonstrates significantly higher correlation coefficients with the direct method than those suggested by Tontini (2007) and Siireli et al. (2007). The proposed method offers advantages in terms of accuracy and survey duration over traditional methods that directly ask for relative importance (e.g., AHP by Saaty (1980)). Furthermore, the integration of the Kano model with IPA or QFD could enhance the accuracy and efficiency of research in determining the importance of characteristics.

• Smart Structures and Systems
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• v.33 no.5
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• pp.333-347
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• 2024

There has been an increasing interest in the construction of smart buildings that can actively monitor and react to their surroundings. The capacity of these intelligent structures to precisely predict and respond to deflection is a crucial feature that guarantees both their structural soundness and efficiency. Conventional techniques for determining deflection often depend on intricate mathematical models and computational simulations, which may be time- and resource-consuming. Artificial intelligence (AI) algorithms have become a potent tool for anticipating and controlling deflection in intelligent structures in response to these difficulties. The term "deflection-aware smart structures" in this sense refers to constructions that have AI algorithms installed that continually monitor and analyses deflection data in order to proactively detect any problems and take appropriate action. These structures anticipate deflection across a range of operating circumstances and environmental factors by using cutting-edge AI approaches including deep learning, reinforcement learning, and neural networks. AI systems are able to predict real-time deflection with high accuracy by using data from embedded sensors and actuators. This capability enables the systems to identify intricate patterns and linkages. Intelligent buildings have the potential to self-correct in order to reduce deflection and maximize performance. In conclusion, the development of deflection-aware smart structures is a major stride forward for structural engineering and has enormous potential to enhance the performance, safety, and dependability of designed systems in a variety of industries.

• Anatomy and Cell Biology
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• v.57 no.3
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• pp.370-377
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• 2024

Anticipating a wide range of morphological variations of arterial anatomy of foregut derivatives beyond the classical pattern, a precise understanding is pertinent to preoperative diagnosis, operative procedure and to avoid potentially devastating post-operative outcome during various traumatic and non-traumatic vascular insult of foregut. The study aimed to revisit the morphological details and update unusual configurations of arteries of foregut to establish clinico-anatomical correlations. This study described the detailed branching pattern of coeliac trunk (CT) as principal artery of foregut with source & course of hepatic, gastric, duodenal and pancreatic branches in 58 cadaveric dissections. Based on morphology, different types and subtypes were made. The descriptions were explained using figures and pertinent tables. Among classical branches of CT, splenic artery was found as most stable whereas other two branches were found to be most variable with missing common hepatic artery in 11 cases. In addition to classical trifurcation (65.52%), different types of bifurcation (12.07%) and tetrafurcations (22.41%) of CT were observed. Regarding variations of hepatic arteries (27.59%), both non-classical origin and accessory hepatic branches were found. In case of gastric branches, more variant origins were seen with right gastric (50%) as compared to left gastric artery (34.48%). Other morphological variations included non-classical origin of gastro-duodenal artery (18.96%) along with presence of accessory pancreatic (17.13%) and duodenal arteries (6.38%). Awareness of anatomical variations regarding circulatory dynamics of foregut is worth knowing in order to facilitate successful planning of surgery involving upper abdominal organs with least complications.

• Journal of the Korean Institute of Gas
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• v.14 no.1
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• pp.28-36
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• 2010

We have garnered 3,593 data of gas accidents reported for 12 years from 1995, and then analyzed the LPG vaporizer accidents according to their types and causes based on the classified database. According to the results the gas rupture has been the most common accident followed by the release, explosion and then fire accidents, the most frequent accident-occurring sub-cause is LPG check floater faults. In addition, we have applied the Poisson Probability Functions to predict the most-likely probabilities of fire, explosion, release and rupture with the LPG vaporizer in the upcoming 5 years. In compliance with Poisson Probability Functions results, in the item which occurs below 3 "LPG-Vaporizer-Fire", in the item which occurs below 5 "LPG-Vaporizer-Products Faults-Check Floater" and the item which occurs below 10 appeared with "LPG-Vaporizer-Products Faults". From this research we have assured the successive database updating will highly improve the anticipating probability accuracy and thus it will play a key role as a significant safety- securing guideline against the gas disasters.

• Journal of the Korea Convergence Society
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• v.5 no.2
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• pp.13-18
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• 2014

In this study, two models due to the configuration of ripper at excavator are investigated by structural and fatigue analyses. The maximum stress and deformation are happened at the axis connected with the body of working device and the direct working part respectively. Model 1 is thought to have more structural durability than model 2. Fatigue life or damage in case of 'SAE bracket history' whose load change is most severest among non-uniform fatigue loads is shown to become most unstable. But life or damage in case of 'Sample history' whose load change is slowest among non-uniform fatigue loads is shown to become most stable. These study results can be effectively utilized with the design of ripper at excavator by anticipating and investigating prevention and durability against its fatigue damage.

• Asian-Australasian Journal of Animal Sciences
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• v.16 no.11
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• pp.1619-1624
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• 2003

The effect of maize hybrid (Suco and Dekalb 765, DK 765), maturity stage (milk, $R_3$ and 1/2 milk line, $R_5$) and animal diet (Diet 1: 70% lucerne hay+30% maize silage; Diet 2: 50% maize silage+20% sunflower meal+30% maize grain) on ruminal stover dry matter (DM) degradability was studied. Additionally, morphological and chemical plant composition was evaluated. Fodder samples ground at 2 mm were incubated in three Holstein steers (400 kg body weight) using the in situ technique. Ruminal degradation kinetics was studied and the effective degradability (ED) was estimated for an assumed kp of 5%/h. The in situ data was analyzed in a complete randomized block design with the animals as blocks. Significant interactions between hybrid${\times}$diet and maturity${\times}$diet on kinetic digestion parameters were detected. In Diet 1, hybrids did not differ in degradable fraction, kd or ED, although a minor difference (p<0.05) in the soluble fraction was found (25.5 and 23.2% for Suco and DK 765, respectively). In Diet 2, the DK 765 had greater degradable fraction (p<0.001) but smaller (p<0.01) kd than Suco, without differences in the soluble fraction or in ED. Anticipating the harvest increased ED of stover from 37.5% in $R_5$ to 44.6% in $R_3$ (average values across hybrids and diets) due to the increase (p<0.001) in the soluble fraction ($R_5$: 22.6%, $R_3$: 28.8%). It is concluded that hybrids had similar stover in situ DM degradability and that soluble fraction represent the main proportion of degradable substrates. Advancing the date of harvesting may not improve the in situ DM degradability of whole maize plant silage since the increase in stover quality is counteracted by the depression in the grain-to-stover ratio. The diet of the animal consuming silage might not improve stover utilization either.