• Journal of Technologic Dentistry
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• v.46 no.2
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• pp.21-27
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• 2024

Purpose: This study aimed to analyze the stress distribution and deformation in implant abutments made from titanium (Ti-6Al-4V), zirconia, and polyetheretherketone (PEEK), including their screws and fixtures, under various loading conditions using finite element analysis (FEA). Methods: Three-dimensional models of the mandible with implant abutments were created using Siemens NX software (NX10.0.0.24, Siemens). FEA was conducted using Abaqus to simulate occlusal loads and assess stress distribution and deformation. Material properties such as Young's modulus and Poisson's ratio were assigned to each component based on literature and experimental data. Results: The FEA results revealed distinct stress distribution patterns among the materials. Titanium alloy abutments exhibited the highest stress resistance and the most uniform stress distribution, making them highly suitable for long-term stability. Zirconia abutments showed strong mechanical properties with higher stress concentration, indicating potential vulnerability to fracture despite their aesthetic advantages. PEEK abutments demonstrated the least stress resistance and higher deformation compared to other abutment materials, but offered superior shock absorption, though they posed a higher risk of mechanical failure under high load conditions. Conclusion: The study emphasizes the importance of selecting appropriate materials for dental implants. Titanium offers durability and uniform stress distribution, making it highly suitable for long-term stability. Zirconia provides aesthetic benefits but has a higher risk of fracture compared to titanium. PEEK excels in shock absorption but has a higher risk of mechanical failure compared to both titanium and zirconia. These insights can guide improved implant designs and material choices for various clinical needs.

• Journal of Korean Society for Quality Management
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• v.51 no.4
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• pp.663-675
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• 2023

Purpose: Skewness is an indicator used to measure the asymmetry of data distribution. In the past, product quality was judged only by mean and variance, but in modern management and manufacturing environments, various factors and volatility must be considered. Therefore, skewness helps accurately understand the shape of data distribution and identify outliers or problems, and skewness can be utilized from this new perspective. Therefore, we would like to propose a statistical quality control method using skewness. Methods: In order to generate data with the same mean and variance but different skewness, data was generated using normal distribution and gamma distribution. Using Minitab 18, we created 20 sets of 1,000 random data of normal distribution and gamma distribution. Using this data, it was proven that the process state can be sensitively identified by using skewness. Results: As a result of the analysis of this study, if the skewness is within ± 0.2, there is no difference in judgment from management based on the probability of errors that can be made in the management state as discussed in quality control. However, if the skewness exceeds ±0.2, the control chart considering only the standard deviation determines that it is in control, but it can be seen that the data is out of control. Conclusion: By using skewness in process management, the ability to evaluate data quality is improved and the ability to detect abnormal signals is excellent. By using this, process improvement and process non-sub-stitutability issues can be quickly identified and improved.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.47 no.2
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• pp.155-167
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• 2024

Distribution and logistics industries contribute some of the biggest GDP(gross domestic product) in South Korea and the number of related companies are quarter of the total number of industries in the country. The number of retail tech companies are quickly increased due to the acceleration of the online and untact shopping trend. Furthermore, major distribution and logistics companies try to achieve integrated data management with the fulfillment process. In contrast, small and medium distribution companies still lack of the capacity and ability to develop digital innovation and smartization. Therefore, in this paper, a deep learning-based demand forecasting & recommendation model is proposed to improve business competitiveness. The proposed model is developed based on real sales transaction data to predict future demand for each product. The proposed model consists of six deep learning models, which are MLP(multi-layers perception), CNN(convolution neural network), RNN(recurrent neural network), LSTM(long short term memory), Conv1D-BiLSTM(convolution-long short term memory) for demand forecasting and collaborative filtering for the recommendation. Each model provides the best prediction result for each product and recommendation model can recommend best sales product among companies own sales list as well as competitor's item list. The proposed demand forecasting model is expected to improve the competitiveness of the small and medium-sized distribution and logistics industry.

• Journal of Distribution Science
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• v.22 no.9
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• pp.129-139
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• 2024

Purpose: This research aims to examine the impact of social media marketing on buy decisions in Indonesia's B2B market, considering the mediating roles of brand image, perceived quality, and perceived value in relation to interactive flat panel display technology. To better understand technology adoption and distribution, we utilize the innovation diffusion theory. Research Design, Data and Methodology: The Decision-Making Unit of each organization that buy interactive flat panel display technology conducted an empirical survey of 82 participants. The quantitative research design analyzed the data utilizing the PLS-SEM model. outcome: This research reveals that social media marketing significantly impacted perceived quality, brand image, perceived value, and buy decisions. The research also found that perceived quality does not significantly impact buy decisions, but perceived value and brand image significantly impacted buy decisions. This research contributes to understanding the key factorsinfluencing buy decisionsin Indonesia's B2B market. Conclusion: Thisresearch concludesthat B2B consumers in Indonesia are less concerned about product quality but prioritize the value they receive when purchasing interactive display technology. Social media marketing could impacted the distribution of interactive display technology in Indonesia's B2B market by affecting the DMU's purchasing decisions. Brands should leverage social media marketing to positively impact theirsuccess.

• Journal of the Korean Geotechnical Society
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• v.40 no.5
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• pp.131-142
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• 2024

To analyze the preloading effects and the load distribution ratio between existing piles and reinforcing piles during vertical extension remodeling, pilot tests were conducted. Previous studies on load distribution typically involved small-scale experiments or numerical simulations. The stiffness of piles used in load distribution designs is often estimated using empirical formulas, which exhibit significant variability. In this study, steel reinforcing piles were installed adjacent to existing PHC piles at an apartment construction site. The actual stiffness of the individual piles was determined through static load tests and compared with empirical formula predictions. The pilot tests (full-scale load-distribution tests), applying column loads simultaneously to the existing and reinforcing piles, demonstrated that column loads are distributed based on the stiffness ratio of each pile. The pilot test outcomes were further compared with long-term measurement data. A smart preloading system was developed in this study to address the preloading loss observed in previous systems, and its applicability was validated through full-scale tests.

• Journal of Distribution Science
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• v.7 no.4
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• pp.47-56
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• 2009

The purpose of this study is to present an alternative improving the efficient and reasonable of the physical distribution system management is influenced by many factors. Therefore, the study depends on the documentary method and survey method to achieve the purpose of this study. The major components of a physical distribution system are refers to as elements, include warehouse·storage system, transportation system, inventory system, physical distribution information system. The factors used in this study are ① factor of product(quality·A/S·added value of product·adaption of product·technical competitive power to other enterprises), ② factor of market(market channel·kinds of customer·physical distribution share), ③ factor of warehouse·storage(warehouse design·size·direction·storage ability·warehouse quality), ④ factor of transportation(promptness·reliability·responsibility·kinds of transportation·cooperation united transportation system·national transportation network), ⑤ factor of packaging (packaging design·material·educating program·pollution degree measure program), ⑥ factor of inventory(ordinary inventory criterion·consistence for inventories record), ⑦ factor of unloaded(unloaded machine·having machine ratio), ⑧ factor of information system (physical distribution quantity analysis·usable computer part), ⑨ factor of physical distribution cost(sales ratio to product) ⑩ factor of physical distribution system(physical distribution center etc). The implication of this study can be summarized as follows: ① In firms that have not adopted a systems integrative approach, physical distribution is a fragmented and often uncoordinated set of activities spread throughout various functions with function having its own set of priorities and measurements. ② The physical distribution is recognized as more an important strategic factor than a simple cost reduction factor, ③ It can be used a strategic competition tool to enterprise.

• KIEE International Transactions on Power Engineering
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• v.3A no.3
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• pp.124-129
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• 2003

As the power industry moves towards open competition, there has been a call for methodology to evaluate power system reliability by using composite interruption cost. This paper presents algorithms to evaluate the interruption cost of distribution power systems by taking into consideration the failure source and the composite customer interruption cost. From the consumer's standpoint, the composite customer interruption cost is considered as the most valuable index to estimate the reliability of a power distribution system. This paper presents new algorithms that take into account the load by customer type and failure probability by distribution facilities while calculating the amount of unserved energy by customer type. Finally, evaluation results of unserved energy and system interruption cost based on composite customer interruption cost are shown in detail.

• Biotechnology and Bioprocess Engineering:BBE
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• v.8 no.2
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• pp.117-125
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• 2003

Experimental and theoretical works were performed for the separation of large polyelectrolyte, such as DNA, in a column packed with gel particles under the influence of an electric field. Since DNA quickly orient in the field direction through the pores, this paper presents how intraparticle convection affects the residence time distribution of DNAs in the column. The concept is further illustrated with examples from solid -liquid systems, for example, from chromatography Showing how the column efficiency is improved by the use of a n electric field. Dimensionless transient mass balance equations were derived, taking into consideration both diffusion and electrophoretic convection. The separation criteria are theoretically studied using two different Peclet numbers in the fluid and solid phases. These criteria were experimentally verified using two different DNAs via electrophoretic mobility measurements. which showed how the separation position of the DNAs varies in the column in relation to the Peg/Pef values of an individual DNA. The residence time distribution was solved by an operator theory and the characteristic method to yield the column response.

• Communications for Statistical Applications and Methods
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• v.19 no.4
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• pp.591-595
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• 2012

We frequently use Tukey's boxplot to identify outliers in the batch of observations of the continuous variable. In doing so, we implicitly assume that the underlying distribution belongs to the family of normal distributions. Such a practice of data handling is often superficial and improper, since in reality too many variables manifest the skewness. In this short paper, we build a modified boxplot and set the outlier identification procedure by assuming that the observations are generated from the skew normal distribution (Azzalini, 1985), which is an extension of the normal distribution. Statistical performance of the proposed procedure is examined with simulated datasets.

• Industrial Engineering and Management Systems
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• v.13 no.3
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• pp.297-303
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• 2014

Stock pre-positioning is one of the most important decisions for preparing the stage of emergency logistics planning. In this paper, a mixed integer model for stock pre-positioning is derived to support an emergency disaster relief response against the event of earthquake. A maximum response time limit, budget availability, multiple item types, and capacity restrictions are considered. In the model, the decision of the distribution centers to cover a disaster area and the amount of supplies to be stocked in each distribution center are simultaneously determined to maximize the total expected relief demand of the disaster areas covered by the existing distribution centers. The proposed model is applied to a real case with 33 disaster areas and 16 distribution centers in Indonesia. Several sensitivity analyses are conducted to estimate the fluctuation on the emergency stock pre-positioning planning by changing the maximum response time and budgets.