• Design Convergence Study
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• v.18 no.3
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• pp.71-87
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• 2019

Previous research has shown that the use of self-disclosure, the process of revealing personal thoughts and feelings, in conversational agents (CAs) increases overall user evaluations. However, research exploring the effects of self-disclosure in different situations or dialogue types is limited. This study investigated the effects of self-disclosure and dialogue type (task- vs. social-oriented) on trust, usefulness, and usage intention. Results showed significant interaction effects between self-disclosure and dialogue type. For CAs that did not use self-disclosure, trust, usefulness, and usage intention were higher in task-oriented dialogues. In contrast, CAs that did use self-disclosure had higher trust, usefulness, and usage intention in social-oriented dialogues. These results suggest that researchers and designers should consider the specific dialogue types and corresponding user goals when adding human qualities, such as self-disclosure, to CAs.

• Asia Marketing Journal
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• v.26 no.1
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• pp.11-22
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• 2024

In the uncertainty fueled by the COVID-19 pandemic, mergers and acquisitions (M&As) have emerged as key strategic responses by firms. This study explores the impact of M&As on acquirers' firm value, utilizing a firm-level panel dataset from SDC Platinum. Empirical evidence recognizes the potential negative impact of transaction value in M&As and the pandemic's effect on market uncertainty that may occasionally exacerbate the adverse influence on acquirers' firm value. The findings indicate that effective marketing strategies, such as enhancing consumer awareness through increasing advertising expenditures, can counterbalance these influences, particularly during uncertain times. This study accentuates the importance of adaptability and a responsive marketing approach in managing M&As during a global crisis. It provides valuable perspectives on consumer awareness in strategic decision-making, offering insights for both academic and business communities and focusing on actionable strategies for navigating the global market turmoil transformed by COVID-19.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.11
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• pp.1785-1790
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• 2014

This study was carried out to compare the physicochemical characteristics and sensory qualities of Sikhye (Korean traditional rice beverage) prepared with foxtail millet, proso millet, and sorghum. The cultivated varieties were Setaria italica Beauv. cv. Samdamae (SDM) and Samdachal (SDC), Panicum miliaceum L. cv. Ibaekchal (IBC), Sorghum bicolor (L.) Moench cv. Nampungchal (NPC), Donganme (DAM), Oryza sativa cv. Ilpum (IP), and Baegokchal (BOC). The brix degrees of SDM, SDC, IBC, NPC, DAM, IP, and BOC Sikhye were 9.53, 8.63, 5.67, 7.57, 6.27, 12.50, and $12.27^{\circ}Bx$, respectively. There were no significant differences in pH (5.99~6.10) among the groups. The highest turbidity was 1.07 in DAM Sikhye. The L-value, a-value, and b-value were 30.85~41.11, -0.34~2.52, and 2.56~5.67, respectively. Total polyphenol contents of SDM, SDC, IBC, NPC, DAM, IP, and BOC Sikhye were 241.52, 213.69, 202.34, 258.25, 193.24, 160.81, and $170.31{\mu}g\;GAE/mL$, respectively. Total flavonoid contents of Sikhye were $19.21{\sim}54.30{\mu}g\;CE/mL$. The highest DPPH and ABTS radical scavenging activities were $16.85{\pm}0.39$ and $64.75{\pm}2.92mg\;TE/100mL$ in DAM Sikhye, respectively. Finally, the sensory evaluation results indicate that there were significant differences in appearance, aroma, and taste between the groups, and SDM Sikhye was similar with IP and BOC.

• Biomolecules & Therapeutics
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• v.10 no.2
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• pp.104-113
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• 2002

The effects of enzyme inhibitors and penetration enhancers on the permeation of leucine enkephalin (Leu-Enk) and its synthetic analog, [${D-ala}^2$]-leucine enkephalinamide (YAGFL) across the nasal, rectal and vaginal mucosae were evaluated. Enzyme inhibitors and penetration enhancers employed for Leu-Enk permeation study were amastatin(AM), thimerosal(TM) and ethylenediaminetetraacetic acid disodium salt(EDTA), and sodium taurodihydrofusidate (STDHF). Those for YAGFL permeation study were TM, benzalkonium chloride(BC) and EDTA, and STDHF, sodium deoxycholate(SDC), sodium glycholate(SGC), glycyrrhizic acid ammonium salt (GAA), L-$\alpha$-Iysophosphatidylcholine(LPC) and mixed micelle (MM, STDHF: linoleic acid = 15 mM : 5 mM). The addition of TM alone on the donor and receptor solutions for Leu-Enk permeation study across all the three kinds of mucosae failed to inhibit the degradation; it completely degraded in 6 hrs, and no permeation occurred. However, with addition of three kinds of inhibitors together, the fluxes across nasal, rectal and vaginal mucosae were $\20.7{pm}2.5$>/TEX>,$\0.3{pm}0.05$>/TEX> and $\1.4{pm}0.5$ $\mu$\mid$textrm{m}$/$\textrm{cm}^2$/hr, respectively. Moreover, the addition of STDHF in the presence of the above three inhibitors enhanced permeation across nasal, rectal and vaginal mucosae 1.3, 15 and 1.3 times, respectively. YhGFL also degraded in the donor and receptor solutions rapidly as time went. With mixed inhibitors of TM and EDTA, the percents of YAGFL remaining in the donor solutions facing nasal, rectal and vaginal mucosae were 69.7, 69.8 and 79.8%, respectively; the percent permeated increased to 10, 2.1 and 5.7%, respectively. The addition of STDHF in the presence of either BC/EDTA or TW/EDTA increased the permeation 2.2, 11.0 and 2.9 times, and 2.21, 14.0 and 2.7 times for nasal, rectal and vaginal mucosae, respectively. With SDC, SGC, GAA, LPC ud MM in the presence of TM/EDTA increased permeation; especially, they increased permeation across vaginal mucosae effectively, and the enhancement factors were 12.5, 7.6, 8.7, 5.7 and 5.5, respectively. The degradation extent of YAGFL was correlated with protein concentrations in the epidermal and serosal extracts. The flux of YAGFL across nasal mucosa increased dose-dependently.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.239-240
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• 2012

With advancements in semiconductor device technologies, manufacturing processes are getting more complex and it became more difficult to maintain tighter process control. As the number of processing step increased for fabricating complex chip structure, potential fault inducing factors are prevail and their allowable margins are continuously reduced. Therefore, one of the key to success in semiconductor manufacturing is highly accurate and fast fault detection and classification at each stage to reduce any undesired variation and identify the cause of the fault. Sensors in the equipment are used to monitor the state of the process. The idea is that whenever there is a fault in the process, it appears as some variation in the output from any of the sensors monitoring the process. These sensors may refer to information about pressure, RF power or gas flow and etc. in the equipment. By relating the data from these sensors to the process condition, any abnormality in the process can be identified, but it still holds some degree of certainty. Our hypothesis in this research is to capture the features of equipment condition data from healthy process library. We can use the health data as a reference for upcoming processes and this is made possible by mathematically modeling of the acquired data. In this work we demonstrate the use of recurrent neural network (RNN) has been used. RNN is a dynamic neural network that makes the output as a function of previous inputs. In our case we have etch equipment tool set data, consisting of 22 parameters and 9 runs. This data was first synchronized using the Dynamic Time Warping (DTW) algorithm. The synchronized data from the sensors in the form of time series is then provided to RNN which trains and restructures itself according to the input and then predicts a value, one step ahead in time, which depends on the past values of data. Eight runs of process data were used to train the network, while in order to check the performance of the network, one run was used as a test input. Next, a mean squared error based probability generating function was used to assign probability of fault in each parameter by comparing the predicted and actual values of the data. In the future we will make use of the Bayesian Networks to classify the detected faults. Bayesian Networks use directed acyclic graphs that relate different parameters through their conditional dependencies in order to find inference among them. The relationships between parameters from the data will be used to generate the structure of Bayesian Network and then posterior probability of different faults will be calculated using inference algorithms.

• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• Membrane Journal
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• v.27 no.2
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• pp.154-166
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• 2017

Polymeric films for gas barrier applications such as food packaging and electronic devices have attracted great interest due to their cheap, light and easy processability among gas barrier materials. Especially in electronic devices, extremely low gas permeance is necessary for maintaining the device performance. However, current polymeric barrier films still suffer from relatively high gas permeance than other materials. Therefore, there have been strong needs to enhance the gas barrier performance of polymeric barrier films while keep their own advantages. Recently, graphene is highlighted as a 2D-layered material for gas barrier applications. However, owing to the poor workability and difficulty to produce in engineering scale, graphene oxide (GO) is on the rise. GO consists of oxygen-containing functional groups on surface with intrinsic 2D-layered structure and high aspect ratio, and it can be well-dispersed in aqueous polar solvents like water, resulting in scalable mass production. Here, we prepared GO incorporated polyimide (PI) nanocomposites. PI is widely used barrier polymer with high mechanical strength and thermal and chemical stability. We demonstrated that PI/GO nanocomposites could perform as a gas barrier. Furthermore, surfactants (Triton X-100 (TX) and Sodium deoxycholate (SDC)) are introduced to enhance the gas barrier performance by improving the degree of dispersion of GO in PI matrix. As a result, TX enhanced the gas barrier performance of PI/GO nanocomposites which is similar to predicted value. This finding will provide new insight to polymer nanocomposites for gas barrier applications.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.3A
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• pp.368-376
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• 2006

This paper describes a Fcore_GenSim(Parameterized FFT Core Generation & Simulation Program), which can be used as an essential If(Intellectual Property) in various OFDM modem designs. The Fcore_Gensim is composed of two parts, a parameterized core generator(PFFT_CoreGen) that generates Verilog-HDL models of FFT cores, and a fixed-point FFT simulator(FXP_FFTSim) which can be used to estimate the SQNR performance of the generated cores. The parameters that can be specified for core generation are FFT length in the range of 64 ~2048-point and word-lengths of input/output/internal/twiddle data in the range of 8-b "24-b with 2-b step. Total 43,659 FFT cores can be generated by Fcore_Gensim. In addition, CBFP(Convergent Block Floating Point) scaling can be optionally specified. To achieve an optimized hardware and SQNR performance of the generated core, a hybrid structure of R2SDF and R2SDC stages and a hybrid algorithm of radix-2, radix-2/4, radix-2/4/8 are adopted according to FFT length and CBFP scaling.

• BMB Reports
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• v.44 no.9
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• pp.578-583
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• 2011

Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) are biochemical markers used to test for liver diseases. Copy number variation (CNV) plays an important role in determining complex traits and is an emerging area in the study various diseases. We performed a genome-wide association study with liver function biomarkers AST and ALT in 407 unrelated Koreans. We assayed the genome-wide variations on an Affymetrix Genome-Wide 6.0 array, and CNVs were analyzed using HelixTree. Using single linear regression, 32 and 42 CNVs showed significance for AST and ALT, respectively (P value < 0.05). We compared CNV-based genes between the current study (KARE2; AST-140, ALT-172) and KARE1 (AST-1885, ALT-773) using NetBox. Results showed 9 genes (CIDEB, DFFA, PSMA3, PSMC5, PSMC6, PSMD12, PSMF1, SDC4, and SIAH1) were overlapped for AST, but no overlapped genes were found for ALT. Functional gene annotation analysis shown the proteasome pathway, Wnt signaling pathway, programmed cell death, and protein binding.

• Journal of Computing Science and Engineering
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• v.5 no.2
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• pp.121-130
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• 2011

Recent research indicates that transient errors will increasingly become a critical concern in microprocessor design. As embedded processors are widely used in reliability-critical or noisy environments, it is necessary to develop cost-effective fault-tolerant techniques to protect processors against transient errors. The register file is one of the critical components that can significantly affect microprocessor system reliability, since registers are typically accessed very frequently, and transient errors in registers can be easily propagated to functional units or the memory system, leading to silent data error (SDC) or system crash. This paper focuses on investigating the impact of register file soft errors on system reliability and developing cost-effective techniques to improve the register file immunity to soft errors. This paper proposes the register vulnerability factor (RVF) concept to characterize the probability that register transient errors can escape the register file and thus potentially affect system reliability. We propose an approach to compute the RVF based on register access patterns. In this paper, we also propose two compiler-directed techniques and a hybrid approach to improve register file reliability cost-effectively by lowering the RVF value. Our experiments indicate that on average, RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly, without sacrificing the register value integrity.