Small-and-medium sized enterprises(SMEs) represent quite a large proportion of the industry as a whole in terms of the number of enterprises or employees. However researches on information system so far have focused on large companies, probably because SMEs were not so active in introducing information systems as larger enterprises. SMEs are now increasingly bringing in information systems such as ERP(Enterprise Resource Planning Systems) and some of the companies already entered the stage of ongoing use. Accordingly, researches should deal with the use of information systems by SME s operating under different conditions from large companies. This study examined factors and mechanism inducing faithful appropriation of information systems, in particular integrative systems such as ERP, in view of individuals` active feedback-seeking behavior. There are three factors expected to affect end users` feedback-seeking behavior for faithful appropriation of information systems. They are management support, peer IT champ support, and IT staff support. The main focus of the study is on how these factors affect feedback-seeking behavior and whether the feedback-seeking behavior plays the role of mediator for realizing faithful appropriation of information systems by end users. To examine the research model and the hypotheses, this study employed an empirical method based on a field survey. The survey used measurements mostly employed and verified by previous researches, while some of the measurements had gone through minor modifications for the purpose of the study. The survey respondents are individual employees of SMEs that have been using ERP for one year or longer. To prevent common method bias, Task-Technology Fit items used as the control variable were made to be answered by different respondents. In total, 127 pairs of valid questionnaires were collected and used for the analysis. The PLS(Partial Least Squares) approach to structural equation modeling(PLS-Graph v.3.0) was used as our data analysis strategy because of its ability to model both formative and reflective latent constructs under small-and medium-size samples. The analysis shows Reliability, Construct Validity and Discriminant Validity are appropriate. The path analysis results are as follows; first, the more there is peer IT champ support, the more the end user is likely to show feedback-seeking behavior(path-coefficient=0.230, t=2.28, p<0.05). In other words, if colleagues proficient in information system use recognize the importance of their help, pass on what they have found to be an effective way of using the system or correct others' misuse, ordinary end users will be able to seek feedback on the faithfulness of their appropriation of information system without hesitation, because they know the convenience of getting help. Second, management support encourages ordinary end users to seek more feedback(path-coefficient=0.271, t=3.06, p<0.01) by affecting the end users' perceived value of feedback(path-coefficient=0.401, t=6.01, p<0.01). Management support is far more influential than other factors that when the management of an SME well understands the benefit of ERP, promotes its faithful appropriation and pays attention to employees' satisfaction with the system, employees will make deliberate efforts for faithful appropriation of the system. However, the third factor, IT staff support was found not to be conducive to feedback-seeking behavior from end users(path-coefficient=0.174, t=1.83). This is partly attributable to the fundamental reason that there is little support for end users from IT staff in SMEs. Even when IT staff provides support, end users may find it less important than that from coworkers more familiar with the end users' job. Meanwhile, the more end users seek feedback and attempt to find ways of faithful appropriation of information systems, the more likely the users will be able to deploy the system according to the purpose the system was originally meant for(path-coefficient=0.35, t=2.88, p<0.01). Finally, the mediation effect analysis confirmed the mediation effect of feedback-seeking behavior. By confirming the mediation effect of feedback-seeking behavior, this study draws attention to the importance of feedback-seeking behavior that has long been overlooked in research about information system use. This study also explores the factors that promote feedback-seeking behavior which in result could affect end user`s faithful appropriation of information systems. In addition, this study provides insight about which inducements or resources SMEs should offer to promote individual users' feedback-seeking behavior when formal and sufficient support from IT staff or an outside information system provider is hardly expected. As the study results show, under the business environment of SMEs, help from skilled colleagues and the management plays a critical role. Therefore, SMEs should seriously consider how to utilize skilled peer information system users, while the management should pay keen attention to end users and support them to make the most of information systems.
Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.
INJ-I, INJ-E, PFN, BMI, and PRF were selected among the various factors which constitute a digital linear accelerator to find effects on the dose distribution by changing current and voltage within the permitted scale which Mevatron automatically maintained. We measured the absorbed dose using an ion chamber, analyzed the waveform of beam output using an oscilloscope, and measured symmetry and flatness using a dosimetry system. An RFA plus (Scanditronix, Sweden) device was used as a dosimetry system. Then an 0.6cc ion chamber (PR06C, USA), an electrometer (Capintec192, USA), and an oscilloscope (Tektronix, USA) were employed to measure the changes on the dose distribution characteristics by changing the beam-tuning parameters. When the currents and the voltages of INJ-I, INJ-E, PFN, BMI, and PRF were modified, we were able to see the notable change on the dose rate by examining the change of the output pulse using the oscilloscope and by measuring them using the ion chamber. However, the results of energy and flatness graph from RF A plus were almost identical. The factors had fine differences: INJ-I, INJ-E, PFN, BMI, and PRF had 0.01∼0.02% differences in D10/D20, 0.1∼0.2 % differences in symmetry, and 0.1∼0.4% differences in flatness. Since Mevatron controlled itself automatically to keep the reference value of the factor, it was not able to see large differences in the dose distribution. There were fine differences on the dose rate distribution when the voltage and the currents of the digitized factors were modified Nonetheless, a basic operational management information was achieved.
Purpose : Selective inhibition of multiple molecular targets may improve the antitumor activity of radiation. Two specific inhibitors of selective cyclooxygenase-2 (COX-2) and epidermal growth factor receptor (EGFR) were combined with radiation on the HeLa cell line. To investigate cooperative mechanism with selective COX-2 inhibitor and EGFR blocker, in vitro experiments were done. Materials and Methods : Antitumor effect was obtained by growth inhibition and apoptosis analysis by annexin V-Flous method. Radiation modulation effects were determined by the clonogenic cell survival assay. Surviving fractions at 2 Gy (
Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70