This study aims to develop a comprehensive predictive model for Digital Quality Management (DQM) and to analyze the impact of various quality activities on different levels of DQM. By employing the Classification And Regression Tree (CART) methodology, we are able to present predictive scenarios that elucidate how varying quantitative levels of quality activities influence the five major categories of DQM. The findings reveal that the operation level of quality circles and the promotion level of suggestion systems are pivotal in enhancing DQM levels. Furthermore, the study emphasizes that an effective reward system is crucial to maximizing the effectiveness of these quality activities. Through a quantitative approach, this study demonstrates that for ventures and small-medium enterprises, expanding suggestion systems and implementing robust reward mechanisms can significantly improve DQM levels, particularly when the operation of quality circles is challenging. The research provides valuable insights, indicating that even in the absence of fully operational quality circles, other mechanisms can still drive substantial improvements in DQM. These results are particularly relevant in the context of digital transformation, offering practical guidelines for enterprises to establish and refine their quality management strategies. By focusing on suggestion systems and rewards, businesses can effectively navigate the complexities of digital transformation and achieve higher levels of quality management.

It is difficult to optimize the process parameters of directly preparing carbonaceous mesophase (CMs) by solvothermal method using coal tar as raw material. To solve this problem, a Decision Tree model for CMs preparation (DTC) was established based on the relationship between the process parameters and the yields of CMs. Then, the importance of variables in the preparation process for CMs was predicted, the relationship between experimental conditions and yields was revealed, and the preparation process conditions were also optimized by the DTC. The prediction results showed that the importance of the variables was raw material type, solvothermal temperature, solvothermal time, solvent amount, and additive type in order. And the optimized reaction conditions were as follows: coal tar was pretreated by decompress distillation and centrifugation, the solvent amount was 50.0 ml, the solvothermal temperature was 230 °C, and the reaction time was 5 h. These prediction results were consistent with the actual experimental results, and the error between the predicted yields and the actual yields was about − 1.1%. Furthermore, the prediction error of DTC method was within the acceptable range when the data sample sets were reduced to 100 sets. These results proved that the established DTC for chemical process optimization can effectively lessen the experimental workload and has high application value.

Background: Scapular winging (SW) could be caused by tightness or weakness of the periscapular muscles. Although data mining techniques are useful in classifying or predicting risk of musculoskeletal disorder, predictive models for risk of musculoskeletal disorder using the results of clinical test or quantitative data are scarce. Objects: This study aimed to (1) investigate the difference between young women with and without SW, (2) establish a predictive model for presence of SW, and (3) determine the cutoff value of each variable for predicting the risk of SW using the decision tree method. Methods: Fifty young female subjects participated in this study. To classify the presence of SW as the outcome variable, scapular protractor strength, elbow flexor strength, shoulder internal rotation, and whether the scapula is in the dominant or nondominant side were determined. Results: The classification tree selected scapular protractor strength, shoulder internal rotation range of motion, and whether the scapula is in the dominant or nondominant side as predictor variables. The classification tree model correctly classified 78.79% (p = 0.02) of the training data set. The accuracy obtained by the classification tree on the test data set was 82.35% (p = 0.04). Conclusion: The classification tree showed acceptable accuracy (82.35%) and high specificity (95.65%) but low sensitivity (54.55%). Based on the predictive model in this study, we suggested that 20% of body weight in scapular protractor strength is a meaningful cutoff value for presence of SW.

For game design planning education, we researched step by step learning method from storyline setting to game content evaluation. In this process, we developed 'Content Generated Tree' educational model applying the segmentation, classification, and prediction process of decision tree theory. This model is divided into the trunk stage as a story setting, the node generation stage as a content branch, and the conformity assessment. In the node generation stage, there are 'Game Theme' stage for determining the overall direction of the game, 'Interest Element' stage for finding the unique joy of the development game, and 'Game Format' stage for setting the visualization direction. The learner creates several game content combinations through content branching, and evaluates each content combination value. The education model was applied to 19 teams, and the efficiency of the step by step learning process was confirmed.

Proteomics may help to detect subtle pollution-related changes, such as responses to mixture pollution at low concentrations, where clear signs of toxicity are absent. Also proteomics provide potential in the discovery of new sensitive biomarkers for environmental pollution. We utilized SELDI-TOF MS (surface enhanced laser desorption. / ionization time-of-flight mass spectrometry) to analyze the proteomic profile of Heterocypris incongruens exposed to several heavy metals (lead, mercury, copper, cadmium and chromium) and pesticides (emamectin benzoate, endosulfan, cypermethrin, mancozeb and paraquat dichloride). Several highly significant biomarkers were selected to make a model of classification analysis. data sets obtained from H. incongruens exposed to pollutants were investigated for differential protein expression by SELDI-TOF MS and decision tree classification. Decision tree model was developed with training set, and then validated with test set from profiling data of H. incongruens. Machine learning techniques provide a promising approach to process the information from mass spectrometry data. Even thought the identification of protein would be ideal, class discrimination does not need it. In the future, this decision tree model would be validated with various levels of pollutants to apply field samples.

본 논문에서는 결정트리 학습 알고리즘을 활용한 축구 게임 수비 NPC 제어 방법을 제안한다. 제안하는 방법은 실제 게임 사용자들의 이동 방향 패턴과 행동 패턴을 추출하여 결정트리학습 알고리즘에 적용한다. 그리고 학습된 결정트리를 바탕으로 NPC의 이동방향과 행동을 결정한다. 실험결과 제안하는 방법은 결정트리 학습에 시간이 다소 걸리지만, 학습된 결정트리를 바탕으로 이동방향이나 행동을 결정하는 시간은 약 0.001-0.003 ms(밀리초)가 소요되어 실시간으로 NPC를 제어할 수 있었다. 또한, 제안하는 방법은 현재 상태 정보 뿐만 아니라 이를 분석한 관계정보, 이전 상태 정보도 함께 활용하므로, 기존방법인 (Letia98)에 비해 이동방향 결정시 높은 정확도를 나타냈다.

도로교량의 경우 급속한 도시화로 인해 증가한 교통량을 처리하기 위해 교량확폭과 신설교량의 추가 건설 등의 방법이 사용되고 있다. 하지만 현재 국내에서는 확폭 또는 신설 교량의 추가건설의 타당성을 판단하기 위한 합리적인 절차나 기준이 마련되어 있지 않다. 또한 교량 확폭 공사 시에는 일반적인 교량신설 공사에 비해 불확실성을 내포한 사건들이 추가적으로 존재한다. 이에 본 논문에서는 의사결정수 방법을 이용해 교량확장에 따라 발생 가능한 사건의 기대 위험비용을 체계적으로 고려할 수 있는 개선된 형태의 생애주기비용 분석 모델을 제안하였다.