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Game development projects adopt Scrum to leverage their flexibility, as game concepts and the customer preferences are highly abstract and unpredictable. The most desirable features in an original game will not be easily identified during the first phase of development but will emerge later in a clear pattern as developers and testers continuously playtest the game. Thus, game development projects use feedback from game testers to understand what they think of various features and concepts, to obtain a better understanding of problem spaces. This study proposes that game tester feedback moderates the effect of Sprint planning on game quality. A field study was conducted using a pair-matched questionnaire in which 102 game development projects participated. Results showed that Sprint planning has a positive effect on game quality. The results also revealed that iterative feedback has a moderating effect on the relationship between Sprint planning and game quality. Theoretical and practical implications are discussed.

Objectives: Little is known about rectal Chlamydia trachomatis (CT) infection in outpatients attending sexually transmitted disease (STD) clinics in China. In this study, we aimed to explore the clinical and epidemiologic features of rectal CT infection in this population. Methods: A cross-sectional study which was conducted among these patients in Tianjin (northern China) and Guangxi (southern China) from June 2018 to August 2020. Bidirectionally sequences were used to analyze CT ompA genotype. Univariate and multivariate logistic regression analysis were developed to explore the association of different risk factors for urogenital and rectal CT infection. Results: A total of 1,382 patients were included in the final analyses. The prevalence of urogenital and rectal CT was 11.2% (154/1374) and 4.9% (68/1377) respectively. The rectal CT prevalence among female and male patients was 7.8% (60/767) and 1.3% (8/610) respectively. The three most common genotypes in urogenital CT samples were genotype E (41/137, 29.9%), J (27/137, 19.7%) and D (21/137, 15.3%), while the most common genotypes among rectal CT positive samples were genotype J (11/47, 23.4%), E (10/47, 21.3%) and D (9/47, 19.2%). More than 85% (52/60) of women infected with rectal CT were co-infected with urogenital CT. About 90.0% (36/40) of women shared the same genotype with urogenital samples. Age under 25 and infected NG were selected as risk factors for urogenital CT, while female patients and patients infected with urogenital CT were the risk factors for rectal CT infection. High proportion of rectal CT infection had concurrent urogenital CT infection especially in women, and most of the co-infections were shared among the same genotypes. Conclusions: It would be prudent to encourage awareness, introduce detections tests and treatment strategies for rectal CT infection particularly in female patients visiting STD clinics in China, irrespective of presence of rectal symptoms and history of anal intercourse. Funding Information: This work was supported by the Chinese Academy of Medical Sciences Initiative for Innovative Medicine (grant 2016-I2M-3-021). Declaration of Interests: The authors report no conflicts of interest in this work. Ethics Approval Statement: This study was conducted in accordance with the Declaration of Helsinki, reviewed and approved by the Institutional Review Board of the Institute of Dermatology, the Chinese Academy of Medical Sciences and Peking Union Medical College (approval number 2017-LS-021).

After an enterprise builds a data warehouse, it can record information related to customer interactions using structured and unstructured data. The intention is to convert these data into useful information for decision-making to ensure business continuity. Hence, this study proposes a new Chinese text classification model for the project management office (PMO) using fuzzy semantics and text mining techniques. First, content analysis is performed on the unstructured data to convert important textual information and compile it into a keyword index. Next, a classification and decision algorithm for grey situations and fuzzy (GFuzzy) is used to categorize textual data by three characteristics: maximum impact, moderate impact, and minimum impact. The purpose is to analyze consumer behaviors for the accurate classification of customers. Lastly, a more effective marketing strategy is formulated to target the various customer combinations, growth models, and the best mode of service. A company database of interactions with customers is used to construct a text mining model and to analyze the decision process of its PMO. The purpose is to test the feasibility and validity of the proposed model so that enterprises are provided with better marketing strategies and PMO processes aimed at their customers.

In stock market forecasting, high-order time-series models that use previous several periods of stock prices as forecast factors are more reasonable to provide a superior investment portfolio for investors than one-order time-series models using one previous period of stock prices. However, in forecasting processes, it is difficult to deal with high-order stock data, because it is hard to give a proper weight to each period of past stock price, reduce data dimensions without losing stock information, and produce a comprehensive forecasting result based on stock data with nonlinear relationships. Additionally, there are two more drawbacks to past time-series models: (1) some assumptions ( Bollerslev, 1986 ; Engle, 1982 ) about stock variables are required for statistical methods, such as the autoregressive model (AR) and autoregressive moving average (ARMA); (2) numeric time-series models have been presented to deal with forecasting problems for stock markets, but they can not handle the nonlinear relationships within the stock prices. To address these shortcomings, this paper proposes a new time series model, which employs the ordered weighted averaging (OWA) operator to fuse high-order data into the aggregated values of single attributes, a fusion adaptive network-based fuzzy inference system (ANFIS) procedure, for forecasting stock price in Taiwanese stock markets. In verification, this paper employs a seven-year period of the TAIEX stock index, from 1997 to 2003, as experimental datasets and the root mean square error (RMSE) as evaluation criterion. The experimental results indicate that the proposed model is superior to the listing methods in terms of root mean squared error.

In order to evaluate student learning achievement, several aspects should be considered, such as exercises, examinations, and observations. Traditionally, such an evaluation calculates a final score using a weighted average method after awarding numerical scores, and then determines a grade according to a set of established crisp criteria. However, this approach lacks the potential to reflect the individual characteristics of a class compared to others. Several researches have used fuzzy techniques to devise practical methods for evaluating student learning achievement to ascertain linguistic terms that are usually used by teachers to assess student learning achievement. However, these approaches are largely based on expert opinions and require complicated computational processes. In this paper, we present a new method for evaluating student learning achievement using an adaptive ordered weighted averaging operator and K-nearest-neighbor classification method. The proposed method simulates the evaluation behavior of teachers when performing a student achievement evaluation based on a norm-referenced evaluation by identifying situations involving the application of intelligence and provides a useful means to award a reasonable grade to students. Furthermore, the proposed method provides a feedback mechanism to update the norm dataset. Therefore, the repetitious use of the feedback mechanism will gradually strengthen the representativeness of the norm dataset.

In recent years, there have been many time series methods proposed for forecasting enrollments, weather, the economy, population growth, and stock price, etc. However, traditional time series, such as ARIMA, expressed by mathematic equations are unable to be easily understood for stock investors. Besides, fuzzy time series can produce fuzzy rules based on linguistic value, which is more reasonable than mathematic equations for investors. Furthermore, from the literature reviews, two shortcomings are found in fuzzy time series methods: (1) they lack persuasiveness in determining the universe of discourse and the linguistic length of intervals, and (2) only one attribute (closing price) is usually considered in forecasting, not multiple attributes (such as closing price, open price, high price, and low price). Therefore, this paper proposes a multiple attribute fuzzy time series (FTS) method, which incorporates a clustering method and adaptive expectation model, to overcome the shortcomings above. In verification, using actual trading data of the Taiwan Stock Index (TAIEX) as experimental datasets, we evaluate the accuracy of the proposed method and compare the performance with the (Chen, 1996 [7], Yu, 2005 [6], and Cheng, Cheng, & Wang, 2008 [20]) methods. The proposed method is superior to the listing methods based on average error percentage (MAER).

Business operation performance is related to corporation profitability and directly affects the choices of investment in the stock market. This paper proposes a hybrid method, which combines the ordered weighted averaging (OWA) operator and rough set theory after an attribute selection procedure to deal with multi-attribute forecasting problems with respect to revenue growth rate of the electronic industry. In the attribute selection step, four most-important attributes within 12 attributes collected from related literature are determined via five attribute selection methods as the input of the following procedure of the proposed method. The OWA operator can adjust the weight of an attribute based on the situation of a decision-maker and aggregate different attribute values into a single aggregated value of each instance, and then the single aggregated values are utilized to generate classification rules by rough set for forecasting operation performance. To verify the proposed method, this research collects the financial data of 629 electronic firms for public companies listed in the TSE (Taiwan Stock Exchange) and OTC (Over-the-Counter) market in 2004 and 2005 to forecast the revenue growth rate. The results show that the proposed method outperforms the listing methods.

Electronic transaction through e-payment protocol will grow tremendous in the coming years. In this article, by conversely using blind signature, the authors propose a fair electronic payment scheme for electronic commerce, which can ensure two participants' right simultaneously in electronic transaction process. This scheme is different from other existing schemes and it does not require strong trust relation between customer and merchant. In the scheme, a semi-trusted third party (S-TTP) is involved to provide a fair commerce environment. The S-TTP takes part in the protocol on-line and no secure information about the business is leaked. Moreover, the participants do not need to register to S-TTP. This scheme does not require intervention of a third party in case of dispute, if one user cheats or simply crashes.