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This action research study has two aims: (i) to develop a wearable virtual reality language-learning platform for English for specific purposes and (ii) to examine student learning effectiveness and the relationship between self-efficacy and behavioral intention. The participants are 131 university students in Taiwan. A model of wearable virtual reality language-learning based on structural equation modeling is built. A path analysis indicates that self-efficacy directly influences students' perceptions of ease of use of the wearable virtual reality technology and indirectly influences perceived usefulness, attitude, and behavioral intention. Student self-efficacy is slightly above the moderate level. Gender and English proficiency have an effect on student self-efficacy and behavioral intention, whereas students' online and virtual reality learning experiences have no significant effect on these variables. This new environment is shown to facilitate student learning: the pretest and posttest results indicate improvements in lexical and semantic knowledge, reading comprehension, and syntax. The study implies that since self-efficacy is a crucial determinant in student technology use, educators should enhance student self-efficacy in order to encourage them to try innovative technology., Author(s): Chun-Chia Hsu [sup.1] , Yu-Li Chen [sup.2] , Chih-Yung Lin [sup.1] , Wei-chih Lien [sup.2] Author Affiliations: (1) grid.445089.4, 0000 0004 0639 3159, Department of Multimedia and Game Science, [...]

In this paper, we attempt to study the effectiveness of various simple machine learning methods in the prediction of bank failures. From a raw dataset of 10,938 US banks during the period of 2000–2020, we find that machine learning approaches do not really outperform the benchmark of conventional statistical method, logistic regression. However, using PCA to retain relevant variance in variables significantly improve the performance of machine learning methods and raise the out-of-sample accuracy of those method to over 70% to over 80%. Of all the machine learning methods used in this paper, the simple KNN seems to be the best model in forecasting bank failure in the United States. [ABSTRACT FROM AUTHOR]

In this study, we explore the relationship among debt capacity, cash holdings and financial constraints by using the deviation in leverage as a proxy for debt capacity. Our findings show a positive relationship between cash holdings and debt capacity. Furthermore, financially constrained firms benefit more from holding cash that leads to larger increases in debt capacity and easier access to bank loans and credit lines by reducing the heterogeneous beliefs of creditors. Our results indicate that cash holdings complement debt capacity for financially constrained firms. [ABSTRACT FROM AUTHOR]

Aims: Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low‐risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high‐risk lesions. We aimed to develop a machine‐learning algorithm based on whole‐slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision. Methods and Results: Patients diagnosed with ADH/DCIS on breast biopsy were included in this study, comprising 592 (740 slides) and 141 (198 slides) patients in the development and independent testing cohorts, respectively. Histological grading of the lesions was independently evaluated by two pathologists. Clinical information, including biopsy method, lesion size, and Breast Imaging Reporting and Data System (BI‐RADS) classification of ultrasound and mammograms, were collected. Deep DCIS consisted of three deep neural networks to evaluate nuclear grade, necrosis, and stromal reactivity. Deep DCIS output comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. Deep DCIS highly correlated with the pathologists' evaluations of both slide‐ and patient‐level labels. All five parameters of Deep DCIS were significantly associated with upstaging to invasive carcinoma in subsequent wide excisional specimens. Using multivariate logistic regression, Deep DCIS predicted upstaging to invasive carcinoma with an area under the curve (AUC) of 0.81, outperforming pathologists' evaluation (AUC, 0.71 and 0.69). After including clinical and hormone receptor status information, performance further improved (AUC, 0.87). This combined model retained its predictive power in two subgroup analyses: the first subgroup included unequivocal DCIS (excluding cases of ADH and DCIS suspicious for microinvasion) (AUC, 0.83), while the second excluded cases of high‐grade DCIS (AUC, 0.81). The model was validated in an independent testing cohort (AUC, 0.81). Conclusion: This study demonstrated that deep‐learning models can refine histological evaluation of ADH and DCIS on breast biopsies, which may help guide future treatment planning. [ABSTRACT FROM AUTHOR]

In this study, we examine the effects of lead independent directors (LIDs) on firms' earnings management. On the basis of US firms from 2000 to 2018, those with LIDs managed earnings less, as reflected by the absolute value of discretionary accruals. Specifically, we find the effect is likely to be more prominent among firms with less disclosure transparency, weaker corporate governance and higher risk. In addition, we find that firms with LIDs tend to increase R&D and capital expenditures, reduce leverages and enhance firm values. Overall, the findings indicate the monitoring of LIDs can effectively reduce firms' earnings management and enhance firm values. [ABSTRACT FROM AUTHOR]