Mobile Innovation and the Music Business in Japan: The Case of Ringing Tone Melody ("Chaku-Mero") (Research Note)

This paper examines the development process and successful factors of the ringing tone melody downloading service, or "Chaku-Mero," in Japan. Chaku-Mero is a mobile Internet service in which a subscriber could download from a wide selection of music melodies his/her favorite with some fee to get it ring when the mobile phone receives a call message. This service is arguably the most successful m-commerce business in the world. According to three major mobile communication carriers, NTT DoCoMo, KDDI, and J-Phone, Chaku-Mero accounts for 40 to 60% of their paid service sales on the mobile Internet. Industry sources estimate that annual payment for Chaku-Mero reached approximately 80-90 billion yen in 2002 (currently US$1=120yen). Also, it has been argued that the Japanese Chaku-Mero service is the sole example of Internet cultural content business, be it fixed or mobile, in the world that has successfully overcome complicated conflicts and concerns of copyrights among different parties and created a significant market. The paper describes the process of how this business has evolved. It traces back the pre-mobile-Internet phase of related services such as the "Sky Melody" service by J-Phone and the wireless Karaoke business, which served as precursors of Chaku-Mero. Then the paper examines the business structure: the parties involved in the business, their relations, and how values are created and distributed among them. Also, the paper analyzes why some content providers have been more successful than others. A leading Chaku-Mero provider, for example, maintains more than 6.5 million subscribers and annual sales of 12 billion yen. Over all, the paper provides a preliminary study of mobile innovation in the music business, which is a part of a larger study of the history of interactions between technologies to create, record, distribute, and promote music and the music business. It would give some implications for the prospects of mobile Internet businesses for music and other cultural contents.

Business Ecosystem and Reverse Salient: The Development of the Mobile Music Business in Japan and Korea

This paper aims at exploring a mechanism of new business development. To understand how a business develops, we move our analytical focus from the level of a focal business to the level of the “business ecosystem,†a collection of related businesses and institutions. We pay special attention to a slowly advancing component as a “reverse salient.†We comparatively examine the developmental process of the mobile music business in Japan and Korea, and show how the interactions among related businesses and music copyright institutions as a reverse salient shaped the directions and speed of the development in each country

Long-Term Deflection Prediction from Computer Vision-Measured Data History for High-Speed Railway Bridges

Management of the vertical long-term deflection of a high-speed railway bridge is a crucial factor to guarantee traffic safety and passenger comfort. Therefore, there have been efforts to predict the vertical deflection of a railway bridge based on physics-based models representing various influential factors to vertical deflection such as concrete creep and shrinkage. However, it is not an easy task because the vertical deflection of a railway bridge generally involves several sources of uncertainty. This paper proposes a probabilistic method that employs a Gaussian process to construct a model to predict the vertical deflection of a railway bridge based on actual vision-based measurement and temperature. To deal with the sources of uncertainty which may cause prediction errors, a Gaussian process is modeled with multiple kernels and hyperparameters. Once the hyperparameters are identified through the Gaussian process regression using training data, the proposed method provides a 95% prediction interval as well as a predictive mean about the vertical deflection of the bridge. The proposed method is applied to an arch bridge under operation for high-speed trains in South Korea. The analysis results obtained from the proposed method show good agreement with the actual measurement data on the vertical deflection of the example bridge, and the prediction results can be utilized for decision-making on railway bridge maintenance

Bayesian Prediction of Pre-Stressed Concrete Bridge Deflection Using Finite Element Analysis

Vertical deflection has been emphasized as an important safety indicator in the management of railway bridges. Therefore, various standards and studies have suggested physics-based models for predicting the time-dependent deflection of railway bridges. However, these approaches may be limited by model errors caused by uncertainties in various factors, such as material properties, creep coefficient, and temperature. This study proposes a new Bayesian method that employs both a finite element model and actual measurement data. To overcome the limitations of an imperfect finite element model and a shortage of data, Gaussian process regression is introduced and modified to consider both, the finite element analysis results and actual measurement data. In addition, the probabilistic prediction model can be updated whenever additional measurement data is available. In this manner, a probabilistic prediction model, that is customized to a target bridge, can be obtained. The proposed method is applied to a pre-stressed concrete railway bridge in the construction stage in the Republic of Korea, as an example of a bridge for which accurate time-dependent deflection is difficult to predict, and measurement data are insufficient. Probabilistic prediction models are successfully derived by applying the proposed method, and the corresponding prediction results agree with the actual measurements, even though the bridge experienced large downward deflections during the construction stage. In addition, the practical uses of the prediction models are discussed

Technical efficiency of small-scale honey producers in Ethiopia: A stochastic frontier analysis

In this paper, a study is presented of the dynamic behavior of an automatic transmit power control (ATPC) loop in a single fixed wireless system (FWS) link subject to multipath fading and an uncorrelated co-channel interferer that does not use ATPC (this represents a so-called non-ATPC FWS link or a fixed satellite link). Fundamental questions include the sensitivity of an ATPC link to multipath interference and the co-channel interference that may be caused by a non-ATPC interferer. In the context of the present project, a good example of a non-ATPC interferer is a fixed satellite to which one antenna in a fixed microwave link has partial view. A computer model was developed that constitutes a useful tool in describing; simulating and analyzing an ATPC loop in a single FWS link. With the aid of this model, results are presented on the sensitivity of an ATPC loop in a FWS link with respect to channel conditions, non-ATPC interference and parameter settings

A leadership competency model of science and technology parks: the case of Chungbuk Techno Park in Korea

Given their significant regional economic benefits, science and technology parks (STPs) have attracted continuous academic attention. To understand the predictors of performance of STPs, this study focuses on the critical impacts of leaders and develops a leadership competency model specialized for public STP leaders. The competency model identifies the qualities and conditions of an effective leader of public STPs, emphasizing management skills, bottom-up approach, and boundary spanning skills. To provide empirical support, this study adopts the case study approach and performs a behavior event interview with one of the most successful leaders of public STPs in Korea

Do depression, fatigue, and body esteem influence premenstrual symptoms in nursing students?

Purpose The purpose of this study was to investigate factors affecting premenstrual symptoms among nursing students, focusing on depression, fatigue, and body esteem. Methods The participants were 145 nursing students at a university located in Changwon, Korea. Data were collected from November 2 to November 30, 2019 using self-reported structured questionnaires, and analyzed using descriptive statistics, independent t-test, analysis of variance, Pearson correlation coefficients, and multiple regression analysis. Results The mean item score for premenstrual symptoms was 2.52±0.92, indicating a low level. The mean score for depression was 16.05±7.72, and 15.2% of participants were found to be moderately depressed and 9.7% severely depressed. The mean item score for fatigue was 4.84±0.84, indicating a moderate level, and body esteem was 2.94±0.44, indicating a moderate level. The premenstrual symptoms of nursing students showed a statistically significant correlation with depression (r=–.58, p<.001), fatigue (r=.33, p<.001), and body esteem (r=–.28, p<.001). Factors impacting premenstrual symptoms of nursing students were depression (β=.47, p<.001), dysmenorrhea (β=–.18, p=.009), menstrual cycle irregularity (β=.17, p=.013), and body esteem (β=–.14, p=.038). The total explanatory power of these variables was 41.0%. Conclusion Findings from this sample of nursing students suggest that intervention programs to relieve premenstrual symptoms should focus on depression, menstrual cycle regularity, dysmenorrhea, and body esteem

How Well Do Large Language Models Truly Ground?

Reliance on the inherent knowledge of Large Language Models (LLMs) can cause issues such as hallucinations, lack of control, and difficulties in integrating variable knowledge. To mitigate this, LLMs can be probed to generate responses by grounding on external context, often given as input (knowledge-augmented models). Yet, previous research is often confined to a narrow view of the term "grounding", often only focusing on whether the response contains the correct answer or not, which does not ensure the reliability of the entire response. To address this limitation, we introduce a strict definition of grounding: a model is considered truly grounded when its responses (1) fully utilize necessary knowledge from the provided context, and (2) don't exceed the knowledge within the contexts. We introduce a new dataset and a grounding metric to assess this new definition and perform experiments across 13 LLMs of different sizes and training methods to provide insights into the factors that influence grounding performance. Our findings contribute to a better understanding of how to improve grounding capabilities and suggest an area of improvement toward more reliable and controllable LLM applications

An End-to-End Tool for Developing CPSs from Design to Implementation

For a Cyber-Physical System (CPS), the real-time execution must be guaranteed at the design time for the safe and reliable interaction between a Cyber and a Physical System. Thus, simulation method is widely used to verify and validate the behavior of a CPS, in the development process. Commercial tools of today, however, only mimic the functional behavior of the system, not the temporal behavior. Moreover, when the simulation target system is changed, developers have to reconfigure all settings to simulate properly. To overcome this limitation, we introduce our End-to-End Development Tool that can support the functional and temporal co-validation and smooth migration for the change of the simulation target system.OAIID:RECH_ACHV_DSTSH_NO:A201617646RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A077084CITE_RATE:FILENAME:V2CPS_final.pdfDEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:FILEURL:https://srnd.snu.ac.kr/eXrepEIR/fws/file/8cf7890c-46ec-4551-9c3e-2715774d7f34/linkCONFIRM: