The Moral Foundations Dictionary (MFD) is a useful tool for applying the conceptual framework developed in Moral Foundations Theory and quantifying the moral meanings implicated in the linguistic information people convey. However, the applicability of the MFD is limited because it is available only in English. Translated versions of the MFD are therefore needed to study morality across various cultures, including non-Western cultures. The contribution of this paper is two-fold. We developed the first Japanese version of the MFD (referred to as the J-MFD) using a semi-automated method—this serves as a reference when translating the MFD into other languages. We next tested the validity of the J-MFD by analyzing open-ended written texts about the situations that Japanese participants thought followed and violated the five moral foundations. We found that the J-MFD correctly categorized the Japanese participants’ descriptions into the corresponding moral foundations, and that the Moral Foundations Questionnaire (MFQ) scores correlated with the frequency of situations, of total words, and of J-MFD words in the participants’ descriptions for the Harm and Fairness foundations. The J-MFD can be used to study morality unique to the Japanese and also multicultural comparisons in moral behavior.
In a large-scale plant such as a nuclear power plant, thousands of process values are measured for the purpose of monitoring the plant performance and the health of various systems. It is difficult for plant operators to constantly monitor all of the process values. We present a data-driven method to comprehensively monitor a large number of process values and detect early signs of anomalies, including unknown events, with few false positives. In order to learn the complex changing internal state of a nuclear power plant and accurately predict the normal process values, we created a two-stage autoencoder composed of a time window autoencoder and a deviation autoencoder, which is a deep learning network structure corresponding to the characteristics of the process values. We assessed performances of the two-stage autoencoder with simulated process values of a nuclear power plant, a 1,100 MW boiling water reactor having 3,100 analog process values. In situations where it is difficult to predict the normal state (rapid operation mode change, transient state, and small fluctuations in the process values), the two-stage autoencoder properly predicted the normal process values and showed excellent performances with early detection and zero false positives, except for one case. The two-stage autoencoder would be an effective solution for comprehensive plant monitoring and early detection of anomaly signs.
In radiation therapy (RT) of esophageal cancer, CTV to PTV margins are generally isotropic and equal for all patients. However, detailed knowledge of the position variability and tumor motion caused by respiratory motion is lacking. The purpose of this study was to accurately quantify esophageal tumor position variability and respiratory motion and investigate possible surrogate structures for image guidance. Materials/Methods: The first 12 patients enrolled in a prospective cohort study (NCT02139488) were analyzed. Patients were treated with chemo-RT with a radiation dose of 23 to 28Â1.8 Gy combined with weekly carboplatin/ paclitaxel and daily 4D CBCT scans. As soft tissue contrast in CBCT is limited, gold fiducial markers (0.35 x 5 mm) were inserted during endoscopic ultrasonography preferably at the proximal border, in the middle and at the distal border of the tumor before the planning CT was made. The following regions of interest (ROI) were registered for each fraction: bony anatomy (vertebrae), carina, diaphragm, and fiducial markers using a rectangular ROI, and gross tumor volume (GTV), using a shaped ROI ("mask"). These different surrogates for setup and their implication on margins were calculated with statistics of the average residual marker displacement when using the different regions as reference (standard deviations of random [s] and systematic [ P ] errors). Breathing amplitudes also influence margins; therefore, their distribution within the cohort was determined by the fiducial ROI. Subsequently, a planning target volume margin including the average respiratory motion was determined for these scenarios according to the nonlinear van Herk formula. Since esophageal tumors border both to lung and mediastinal tissue, the parameters for this formula were conservatively chosen to be representative for water. Results: A median of 3 fiducials was placed in 12 tumors located at the mid esophagus (nZ3), lower esophagus (nZ4), and gastroesophageal junction (nZ5). The median (range) peak-to-peak respiratory tumor motion amplitude in the left-right (LR), craniocaudal (CC), and anteroposterior (AP) directions was 0.15 (0.07-0.73), 0.63 (0.39-0.95), and 0.30 (0.08-0.79) cm, respectively. The required margin, depending on surrogate used for setup correction, ranged from LR 0.69-0.88 cm and CC 0.76-1.14 cm to AP 0.56-0.7 cm (Table 1). Overall, the registration on mask results in the smallest margins. Conclusion: Substantial position variability of the GTV during RT of esophageal cancer was observed as well as interpatient variation in respiratory-induced motion. Tumor localization is considerably improved, compared to bony setup, when the GTV mask registration is used in CBCT guidance, despite low soft tissue contrast. Furthermore, patient-specific margins are required to mitigate breathing-induced motion.
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