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This multicenter phase I/IIa study aimed to determine the recommended phase II dose (RP2D) and evaluate the safety and preliminary efficacy of liposomal irinotecan (nal-IRI), oxaliplatin, and S-1 (NASOX) as first-line treatment for advanced pancreatic adenocarcinoma. Patients with locally advanced or metastatic pancreatic adenocarcinoma without prior systemic treatment for advanced disease, aged ≥ 19 years, with measurable disease, and Eastern Cooperative Oncology Group performance status of 0–1 were eligible. The primary endpoints were to determine the dose-limiting toxicity (DLT) in the phase I cohort and overall response rate (ORR) in the phase IIa cohort. The intention-to-treat (ITT) analysis included patients who received the RP2D. In phase I, seven patients were screened, and six were assessed for DLT. None experienced DLT during the first cycle. The RP2D was determined as nal-IRI 50 mg/m2 and oxaliplatin 60 mg/m2 on day 1, S-1 40 mg/m2 twice daily on days 1–7 every 14 days. For the ITT (N = 41; 7, and 34 from phases I and IIa, respectively), the most common grade 3–4 treatment-emergent adverse events were neutropenia (31.7 %), enterocolitis (9.8 %), anorexia (7.3 %), and diarrhea (2.4 %). The ORR was 58.5 % (1 complete, and 23 partial responses). Two underwent conversion surgery; both achieved R0 resection. With median follow-up of 17.5 months, median progression-free survival was 6.5 months (95 % confidence interval [CI], 5.0–8.1) and median overall survival was 11.4 months (95 % CI, 9.8–15.5). NASOX exhibited a manageable safety profile and encouraging efficacy outcomes consistent with NALIRIFOX, showing potential to replace infusional 5-fluorouracil with oral S-1 in the triplet regimen. • In this Ph I/IIa trial, patients with advanced PDAC received 1 L NASOX every 2 weeks. • RP2D: nal-IRI 50 mg/m2, oxaliplatin 60 mg/m2 on D1, S-1 40 mg/m2 BID on D1–7. • ORR was 58.5 %, median PFS was 6.5 months, and median OS was 11.4 months. • Safety profiles were manageable. • NASOX may improve patient convenience while showing efficacy comparable to NALIRIFOX. [ABSTRACT FROM AUTHOR]

• We provide empirical tests on various image corruption using vision transformers. • Vision transformers showed performance degradation on contrast-enhanced images. • We proposed PreLayerNorm for the consistent behavior of positional embedding. • We observed that PreLayerNorm improved performance on contrast-enhanced images. • We provide theoretical analyses on the inconsistent behavior of vision transformers. [Display omitted] Vision Transformers (ViTs) have recently demonstrated state-of-the-art performance in various vision tasks, replacing convolutional neural networks (CNNs). However, because ViT has a different architectural design than CNN, it may behave differently. To investigate whether ViT has a different performance or robustness, we tested ViT and CNN under various imaging conditions in practical vision tasks. We confirmed that for most image transformations, ViT's robustness was comparable or even better than that of CNN. However, for contrast enhancement, ViT performed particularly poorly. We show that this is because positional embedding in ViT's patch embedding can work improperly when the color scale changes. We demonstrate that the use of PreLayerNorm, a modified patch embedding structure, ensures the consistent behavior of ViT. Results demonstrate that ViT with PreLayerNorm exhibited improved robustness in the contrast-varying environments. [ABSTRACT FROM AUTHOR]

Predicting electrical power consumption is essential in many industries. This paper presents a method to predict total electrical power consumption in a certain period of Hot Rolling Mill (HRM) in steel mills. We adequately pre-processed operation data, including work schedule, work result, and power measurement. In addition, based on domain knowledge, we derived several useful variables relevant to power consumption. To predict power consumption, we developed a Multi-Level Stacked Regression model that combines the strengths of multiple prediction models. Real-world applications have dataset shift characteristics in which the covariance between features or distribution of the features changes over time due to frequent changes in the working environment. To verify the generalization accuracy of our model, we trained and tested our model for various training/test splits and compared it with several comparative models. Our model showed the best accuracy in all training/test splits. We verified whether the predictive model reflects the real-world environment well by analyzing the actual physical meaning of the essential features used in the predictive model. • A series of pre-processing methods are applied to real data from Hot Rolling Mill. • A Multi-Level Stacked Regression algorithm is proposed. • Performance on various training/test splits that contains dataset shifts is verified. [ABSTRACT FROM AUTHOR]