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Previous research has illustrated the potential of flipped learning for assisting teachers in designing meaningful activities to promote students' higher order thinking skills; however, several previous studies have challenged the effects of flipped learning on students' learning. One of the key problems is the lack of an effective learning approach or tools to engage students in the flipped learning activity. In this study, a concept mapping-based Prediction-Observation-Explanation (POE) approach was incorporated into flipped learning (called CPOE-FL) to enhance students' scientific learning. Furthermore, a three-group experiment was conducted to assess the effects of the three flipped learning models, comprising the CPOE-FL approach, the POE-FL (incorporating POE into flipped learning) approach, and the C-FL (conventional flipped learning) approach. The experimental results displayed that the CPOE-FL approach can benefit the learning achievements and self-efficacy of the students with respectively lower prior knowledge and lower initial self-efficacy, in comparison with the POE-FL and C-FL approaches. Both the CPOE-FL and POE-FL approaches promoted the students' inner learning motivation, while the CPOE-FL approach enhanced the students' critical thinking. This proposed approach could provide a good reference for researchers or school teachers intending to implement POE-based flipped learning in the future.

Radiotherapy combined with a radiosensitizer represents an important treatment for head and neck squamous cell carcinoma (HNSCC). Only a few chemotherapy agents are currently approved as radiosensitizers for targeted therapy. Oral squamous cell carcinoma is one of the deadliest cancers, with approximately ~500,000 new diagnosed cases and 145,000 deaths worldwide per year. The incidence of new cases continues to increase in developing countries. This study aimed to investigate the effect of Croton tonkinensis and Curcuma longa on cell viability in OSCC cells. The HNSCC cell line OML1 and its radiation-resistant clone OML1-R were used. The anticancer effect and the mechanism of action of Croton tonkinensis and Curcuma longa in OSCC cells were analyzed by using cell viability assays, Western blot analysis, and Tranwell migration assays. The results showed that Croton tonkinensis concentration-dependently reduced the viability of OML1 and OML1-R (radioresistant) cells by downregulating the levels of AKT/mTOR mediators, such as p110α, p85, pAKT (ser473), p-mTOR (ser2448), and p-S6 Ribosomal (ser235/236). We found that cotreatment of OML1 and OML1R cells with either zVAD-FMK (apoptosis inhibitor), Ferrostatin-1 (Fer-1, a ferroptosis inhibitor), or chloroquine (CQ, an autophagy inhibitor) markedly reduced cell death. These results demonstrate that Croton tonkinensis exhibits anti-proliferation activity and highlight the therapeutic potential of small-molecule inhibitors against PI3K/mTOR signaling for radiosensitization in HNC treatment. [ABSTRACT FROM AUTHOR]

Pedestrian trajectory prediction is crucial for developing collision avoidance algorithms in autonomous driving systems, aiming to predict the future movement of the detected pedestrians based on their past trajectories. The traditional methods for pedestrian trajectory prediction involve a sequence of tasks, including detection and tracking to gather the historical movement of the observed pedestrians. Consequently, the accuracy of trajectory prediction heavily relies on the accuracy of the detection and tracking models, making it susceptible to their performance. The prior research in trajectory prediction has mainly assessed the model performance using public datasets, which often overlook the errors originating from detection and tracking models. This oversight fails to capture the real-world scenario of inevitable detection and tracking inaccuracies. In this study, we investigate the cumulative effect of errors within integrated detection, tracking, and trajectory prediction pipelines. Through empirical analysis, we examine the errors introduced at each stage of the pipeline and assess their collective impact on the trajectory prediction accuracy. We evaluate these models across various custom datasets collected in Taiwan to provide a comprehensive assessment. Our analysis of the results derived from these integrated pipelines illuminates the significant influence of detection and tracking errors on downstream tasks, such as trajectory prediction and distance estimation. [ABSTRACT FROM AUTHOR]

Background The prognostic value of traditional clinical indicators for locally recurrent nasopharyngeal carcinoma is limited because of their inability to reflect intratumor heterogeneity. We aimed to develop a radiomic signature to reveal tumor immune heterogeneity and predict survival in locally recurrent nasopharyngeal carcinoma. Methods This multicenter, retrospective study included 921 patients with locally recurrent nasopharyngeal carcinoma. A machine learning signature and nomogram based on pretreatment magnetic resonance imaging features were developed for predicting overall survival in a training cohort and validated in 2 independent cohorts. A clinical nomogram and an integrated nomogram were constructed for comparison. Nomogram performance was evaluated by concordance index and receiver operating characteristic curve analysis. Accordingly, patients were classified into risk groups. The biological characteristics and immune infiltration of the signature were explored by RNA-sequencing analysis. Results The machine learning signature and nomogram demonstrated comparable prognostic ability to a clinical nomogram, achieving concordance indexes of 0.729, 0.718, and 0.731 in the training, internal, and external validation cohorts, respectively. Integration of the signature and clinical variables statistically improved the predictive performance. The proposed signature effectively distinguished patients between risk groups with statistically distinct overall survival rates. Subgroup analysis indicated the recommendation of local salvage treatments for low-risk patients. Exploratory RNA-sequencing analysis revealed differences in interferon response and lymphocyte infiltration between risk groups. Conclusions A magnetic resonance imaging–based radiomic signature predicted overall survival more accurately. The proposed signature associated with tumor immune heterogeneity may serve as a valuable tool to facilitate prognostic stratification and guide individualized management for locally recurrent nasopharyngeal carcinoma patients. [ABSTRACT FROM AUTHOR]

Structural health monitoring is conducted to ensure the structural integrity of a building during earthquakes. This study aimed to improve our understanding of the dynamic response of buildings subjected to a series of earthquake excitations, focusing on interpreting structural dynamic characteristics and identifying potential seismic damage subjected to a series of earthquake excitations. To this end, a series of seismic response data of a 13‐story reinforced‐concrete/steel building were collected through long‐term monitoring over 2 years. A systematic approach for monitoring the health of the building was established by integrating several algorithms for vibration‐based (both output‐only and input–output parametric nonparametric feature‐discrimination algorithms) and model‐based feature extraction techniques. Furthermore, the time‐varying dynamic characteristics of the building were determined, including its modal frequency, mode shape, and stiffness, as extracted from features obtained over the monitoring period of 2 years. Safety assessment of this newly compound high‐rise building is investigated to explore the system dynamic characteristics through long‐term seismic monitoring. [ABSTRACT FROM AUTHOR]