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Mobile devices are becoming ubiquitous methodologies and tools, providing application for learning and teaching field. On the basis of the widespread use of wireless devices and mobile computing technology, this study proposes a context-aware plant ecology learning system (CAPELS) based on context-aware technology; adapting deep neural networks (DNN) and leaf vein and shape identification algorithm which can identify plant leaves, this system automatically provides relevant botanical and growth environment knowledge to the learners. Therefore, during outdoor education, it can assist learners in accurately obtaining the required relevant botanical and growth environment knowledge. The experimental results indicate that students who used CAPELS performed better learning about plant ecology than those who did not. We also delivered questionnaires to those who used CAPELS and analyzed the results by using the partial least squares (PLS) method. The results have shown that CAPELS can encourage student's learning motivation and thus improve their learning effectiveness. Thus, CAPELS provides a new educational platform for promoting ecology learning approach and effectively improves student learning efficiency and motivation.

Background: Advanced non-small-cell lung cancer (NSCLC) can be treated with novel targeted therapies that are tailored to the genetic characteristics of malignancy. While tissue-based genomic testing is considered the gold standard for the detection of oncogenic driver mutations, several challenges like inadequate tissue availability, the invasiveness of procuring tumors, and prolonged turnaround time of analysis are encountered. Considering these limitations, guidelines have recognized liquid biopsies using circulating cell-free DNA (cfDNA) as a useful tool to complement conventional tissue testing. Even though cfDNA next-generation sequencing (NGS) can have high sensitivity and specificity, optimal patient benefit requires the interpretation of the molecular profiling results in the context of clinical and diagnostic features to achieve the best outcomes. Case Descriptions: In this case series, we present six patients with advanced NSCLC whose plasma or tissue biopsy samples were analyzed with commercially available comprehensive NGS assays that elucidate the role of testing at various time points in the treatment journey. In all six cases, comprehensive genomic profiling (CGP) provided clinically useful information to guide treatment decisions. Conclusion: Adding to the existing real-world evidence, this case series reinforces that CGP-driven treatment strategies in advanced NSCLC, coupled with other available clinical information, can optimize treatment decisions. [ABSTRACT FROM AUTHOR]

PurposeEpidermal growth factor receptor (EGFR) -tyrosine kinase inhibitors have proven efficacy in advanced non-small-cell lung cancer (NSCLC). We hypothesized that erlotinib would be efficacious in the adjuvant setting.Patients and methodsAn international randomized, double-blind, placebo-controlled study was conducted in patients with completely resected IB to IIIA NSCLC whose tumors expressed EGFR protein by immunohistochemistry or EGFR amplification by fluorescence in situ hybridization. Patients were assigned 2:1 to erlotinib 150 mg once per day or placebo for 2 years. Stratification factors were stage, histology, previous adjuvant chemotherapy, smoking status, EGFR amplification status, and country. The primary end point was disease-free survival (DFS); key secondary end points were overall survival (OS) and DFS and OS in patients whose tumors had EGFR-activating mutations (EGFRm-positive).ResultsA total of 973 patients were randomly assigned (November 26, 2007, to July 7, 2010). There was no statistically significant difference in DFS (median, 50.5 months for erlotinib and 48.2 months for placebo; hazard ratio, 0.90; 95% CI, 0.74 to 1.10; P = .324). Among the 161 patients (16.5%) in the EGFRm-positive subgroup, DFS favored erlotinib (median, 46.4 v 28.5 months; hazard ratio, 0.61; 95% CI, 0.38 to 0.98; P = .039), but this was not statistically significant because of the hierarchical testing procedure. OS data are immature. Rash and diarrhea were common adverse events occurring in 528 (86.4%) and 319 (52.2%) patients treated with erlotinib, respectively, versus 110 (32.1%) and 54 (15.7%) patients receiving placebo. The most common grade 3 adverse events in patients treated with erlotinib were rash (22.3%) and diarrhea (6.2%).ConclusionAdjuvant erlotinib did not prolong DFS in patients with EGFR-expressing NSCLC or in the EGFRm-positive subgroup. Further evaluation of erlotinib is warranted in the EGFRm-positive subgroup.

Counting small pixel-sized vehicles and crowds in unmanned aerial vehicles (UAV) images is crucial across diverse fields, including geographic information collection, traffic monitoring, item delivery, communication network relay stations, as well as target segmentation, detection, and tracking. This task poses significant challenges due to factors such as varying view angles, non-fixed drone cameras, small object sizes, changing illumination, object occlusion, and image jitter. In this paper, we introduce a novel multi-data-augmentation and multi-deep-learning framework designed for counting small vehicles and crowds in UAV images. The framework harnesses the strengths of specific deep-learning detection models, coupled with the convolutional block attention module and data augmentation techniques. Additionally, we present a new method for detecting cars, motorcycles, and persons with small pixel sizes. Our proposed method undergoes evaluation on the test dataset v2 of the 2022 AI Cup competition, where we secured the first place on the private leaderboard by achieving the highest harmonic mean. Subsequent experimental results demonstrate that our framework outperforms the existing YOLOv7-E6E model. We also conducted comparative experiments using the publicly available VisDrone datasets, and the results show that our model outperforms the other models with the highest AP50 score of 52%. [ABSTRACT FROM AUTHOR]

Web-based peer assessment is an innovative method that can be used to assess students' learning portfolios in order to improve their learning. However, students cannot easily compare their own evaluation schemes or think reflectively to improve their learning through web-based peer assessment if they do not understand how others perceive the assessment criteria. This study attempts to utilize knowledge acquisition techniques in order to elicit personal understanding of assessment criteria. Using repertory grid analysis, web-based peer assessment systems can elucidate personal conceptual frameworks and evaluation schemes when students use their own criteria to assess learning portfolios. The analysis provides an instrument for monitoring the students' conceptual frameworks--which may lead to different peer-teacher correlations regarding assessment. This instrument allows teachers and students to understand fully each other's conceptual frameworks and evaluation schemes--thereby allowing them to think reflectively and improve their learning.