The aim of this study was to evaluate the influences of oral candidiasis and herpes simplex virus 1 (HSV-1) infections in chemotherapy-induced oral mucositis (OM). The medical records of 424 consecutive patients with hematological malignancies who had received chemotherapy at a medical center in Taiwan from January 2006 to November 2007 were retrospectively reviewed. The results of swab cultures of fungus and HSV-1 for OM were correlated with associated clinical features. Younger age, myeloid malignancies, and disease status other than complete remission before chemotherapy were significantly correlated with the development of OM. Risks of fever (p < 0.001) and bacteremia were higher in patients with OM. Among 467 episodes of OM with both swab cultures available, 221 were non-infection (47.3%) and 246 were related to either fungal infections, HSV-1 infections, or both (52.7%); of the 246 episodes, 102 were associated with fungal infections alone (21.8%), 98 with HSV-1 infections alone (21%), and 46 with both infections (9.9%). Patients who had received antifungal agents prior to OM occurrence tended to have HSV-1 infection (p < 0.001). Our results suggest that Candida albicans and HSV-1 play an important role in chemotherapy-induced OM in patients with hematological malignancies.

Statistical process control charts are important tools for detecting process shifts. To ensure accurate, responsive fault detection, control chart design is critical. In the literature, control charts are typically designed by minimizing the control chart's responding time, i.e., average run length (ARL), to an anticipated shift size under a tolerable false alarm rate. However, process shifts, originating from various variation sources, often come with different sizes and result in different degrees of quality impacts. In this paper, we propose a new performance measure for EWMA and CUSUM control chart design to take into consideration the variable shift sizes and corresponding quality impacts. Unlike economic designs of control charts that suffer from a complex cost structure and intensive numerical computation, the proposed design methodology does not involve any cost estimation and the design procedure is as simple as looking up tables. Given the Gaussian random shifts and quadratic quality loss function, we show that the proposed design has a significant reduction in the quality impact as compared to conventional ARL-based designs. Guidelines and useful worksheets for practical implementation of the proposed designs are then suggested to practitioners with different knowledge levels of the process excursions.

Abstract. Taiwan is located in subtropical monsoon area and Pacific Ring of Fire. Both the rate of crustal uplift and annual rainfall are among the highest in the world. Earthquakes and heavy rainfall have led to massive landslides and debris flow. Frequent disasters and the high rate of surface erosion have caused drastic changes in river topography and catchment areas, and, consequently, have impacted the safety of human lives. To mitigate the losses, better simulation and prediction of landslides are critical. Existing landslide prediction research works employed terrain, geology, rainfall, earthquakes and human activities as landslide triggering factors in the predicting model. In addition to aforementioned environmental conditions, this study would like to explore the use of SAR differential interferometry (InSAR) information to help observe characteristics of the slope movement behavior, which is also an important factor. Factors are analyzed and quantified on the basis of slope units. To confirm the applicability of selected factors to landslide, factors are firstly analyzed with Spearman correlation, and then those with higher correlations are incorporated into the prediction model. Machine learning based techniques are then employed to establish the prediction model. The experiment result demonstrates that InSAR information can improve the accuracy by more than 5% in landslide prediction.