Despite the accumulating body of knowledge on action research, the scope of research on teacher identity construction in action research is still limited. This study relied on the concept of ...identities-in-practice and examined four second language (L2) teachers’ identity construction across the plan, act, observe, and reflect stages of action research. Data were collected from semi-structured interviews, reflective journals, and classroom observations. Data analyses indicated that the teachers navigated their action research identity construction through the four stages as: plan (managing the misgivings); act (initial puzzlement, subsequent adaptability, and satisfaction); observe (positive emotions and increased agency); and reflect (further initiatives, greater knowledge generation, and enhanced reflexivity). The study concludes with implications for further empirical attention to the role of emotions in teacher-researchers’ action research engagement and the important role of teacher educators in assisting teacher-researchers with taking the initiative toward conducting action research.
Language for Specific Purposes (LSP) has received extensive theoretical and empirical attention across most of its subareas. However, several claims have been raised as to the limited scope of ...research on LSP teachers. The present study is a review of the studies conducted on LSP teacher education from 2000 to 2019 in order to track the scope of the state-of-the-art and peer-reviewed works done in this area. The search for LSP teacher education research yielded 60 studies representing similar foci from which ten categories emerged. The categories included: action research, cognitions, practices, cognitions and practices, content and language teachers, professional development, genre, critical incidents, identity, and language change. It was found that the line of inquiry features a dispersed, intermittent empirical attention to teachers, with a wide array of topics existing within the nomenclature of teacher education being untouched in LSP. Additionally, in comparison to general language teacher education, LSP teacher education has received much less attention, which in turn calls for further attention from researchers to build the associated scholarship in more depth. The study raises implications for LSP teacher education situated within the current understandings of (language) teacher education and highlights the relevant potential research directions.
Purpose
To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade.
Materials and methods
...Seventy-one ccRCC patients (31 low grade and 40 high grade) were included in this study. Tumors were manually segmented on CT images followed by the application of three image preprocessing techniques (Laplacian of Gaussian, wavelet filter, and discretization of the intensity values) on delineated tumor volumes. Overall, 2530 radiomics features (tumor shape and size, intensity statistics, and texture) were extracted from each segmented tumor volume. Univariate analysis was performed to assess the association between each feature and the histological condition. Multivariate analysis involved the use of machine learning (ML) algorithms and the following three feature selection algorithms: the least absolute shrinkage and selection operator, Student’s
t
test, and minimum Redundancy Maximum Relevance. These selected features were then used to construct three classification models (SVM, random forest, and logistic regression) to discriminate high from low-grade ccRCC at nephrectomy. Lastly, multivariate model performance was evaluated on the bootstrapped validation cohort using the area under the receiver operating characteristic curve (AUC) metric.
Results
The univariate analysis demonstrated that among the different image sets, 128 bin-discretized images have statistically significant different texture parameters with a mean AUC of 0.74 ± 3 (
q
value < 0.05). The three ML-based classifiers showed proficient discrimination between high and low-grade ccRCC. The AUC was 0.78 for logistic regression, 0.62 for random forest, and 0.83 for the SVM model, respectively.
Conclusion
CT radiomic features can be considered as a useful and promising noninvasive methodology for preoperative evaluation of ccRCC Fuhrman grades.
Designing a model to predict the main non-dimensional parameters related to the fluids, i.e., the Capillary number, the Reynolds number, flow rates ratio, and viscosities ratio of two fluids, to ...achieve a desired droplet size is so important. We use a soft computing method, i.e., a multi-layer perceptron artificial neural network (MLP-ANN), to extract this model. The model is trained by both experimental and validated simulation data in the COMSOL environment. To optimize the structure of the MLP-ANN, the swarm-based metaheuristic algorithms are used, i.e., Particle Swarm Optimization, Firefly Algorithm (FA), Grey wolf optimizer, and Grasshopper Optimization Algorithm. The results show that the FA algorithm has the best results. The optimized network has two hidden layers, with 6 and 14 neurons in its first and second hidden layers, and the network's transfer functions in its hidden layers are in the type of logsig. The RMSE and
R
2
values for the optimized MLP network are equal to 4.0076 and 0.9900, respectively. Then, the inverse model is used to calculate the optimum parameters related to the fluids to achieve a desired droplet size. This problem is solved as an optimization problem. A 3D diagram of these optimal parameters is plotted for five desired droplet sizes. It can be seen that these optimal points create different zones related to each droplet size. In other words, for a desired droplet size, there is a confined zone in the 3D space of the capillary number, flow rates ratio, and viscosities ratio.
Despite the widely recognized significance of critical incidents (CIs) in teachers’ professional learning, little research has investigated the role of CIs in language teacher identity development. ...This study attempts to fill this gap by exploring the contributions of a Telegram-based professional development course—framed around CI storying—to the language teacher identity development process of a group of teachers. Data were collected from 10 teachers before, during, and after the course. Data analyses indicated that, before the course, CIs negatively influenced the teachers’ agency and emotions. Participation in the course contributed, however, to the teachers’ enhanced agency and greater emotion regulation. In addition, the course afforded the teachers an opportunity to experience further professional socialization and collegial engagement. Our findings revealed that during the course, the teachers developed greater expertise in storying their CIs and discussed higher order issues relevant to the multiplicity of identity as connected to sociocultural-educational dimensions. These findings suggest that emotions and agency are two significant identity aspects that are profoundly influenced by and influence CIs. Our article closes with a discussion of the implications of embedding CIs in professional development courses to help teachers (re)construct their identities.
Despite the growth of research on EAP teachers in the past decade, little research has focused on their emotions and much less on their well-being. In response to this gap of knowledge, the present ...study draws on the theoretical framework of activity theory and explores the well-being of 13 Iranian EAP teachers. We collected data from a questionnaire, narrative frames, and semi-structured interviews. Data analyses revealed three themes in relation to the teachers’ well-being: (1) content as a site of experiencing positive and negative emotions, (2) content and institutional contextualities as determinants of seeking purpose in EAP instruction, and (3) sociocultural parameters as shaping meaning in EAP instruction. We found that EAP teacher well-being functions as a layered construct that is influenced by various personal, institutional, and sociocultural dynamics, and substantially influences teachers’ professional practices, identities, and emotions. Across these processes, content operates as the antecedent or consequence of the influence in EAP instruction. Based on the findings, we provide implications for teachers and teacher educators in how to employ professional alternatives that could effectively contribute to EAP teachers’ well-being.
The escalating integration of artificial intelligence (AI) technologies, particularly the widespread use of ChatGPT in higher education, necessitates a profound exploration of effective communication ...strategies. This paper addresses the critical role of prompt development as a skill essential for university instructors engaging with ChatGPT. While emphasizing the practical implications for higher education, the study introduces a novel two-layered AI prompt formula, considering both components and elements. In methodology, the research synthesizes insights from existing models and proposes a tailored approach for ChatGPT, addressing its unique characteristics and the contextual elements within higher education. The results highlight the formula’s flexibility and potential applications in diverse fields, from syllabus planning to assessment. Moreover, the study identifies limitations inherent in ChatGPT, emphasizing the need for instructors to exercise caution in its usage. In conclusion, the paper underscores the evolving landscape of AI in education, envisaging specialized versions of ChatGPT for academic settings and advocating for the proactive adoption of ethical frameworks in the use of AI in higher education. This study serves as a foundational contribution to the discourse on effective AI communication in educational settings.
To investigate the impact of harmonization on the performance of CT, PET, and fused PET/CT radiomic features toward the prediction of mutations status, for epidermal growth factor receptor (EGFR) and ...Kirsten rat sarcoma viral oncogene (KRAS) genes in non-small cell lung cancer (NSCLC) patients.
Radiomic features were extracted from tumors delineated on CT, PET, and wavelet fused PET/CT images obtained from 136 histologically proven NSCLC patients. Univariate and multivariate predictive models were developed using radiomic features before and after ComBat harmonization to predict EGFR and KRAS mutation statuses. Multivariate models were built using minimum redundancy maximum relevance feature selection and random forest classifier. We utilized 70/30% splitting patient datasets for training/testing, respectively, and repeated the procedure 10 times. The area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess model performance. The performance of the models (univariate and multivariate), before and after ComBat harmonization was compared using statistical analyses.
While the performance of most features in univariate modeling was significantly improved for EGFR prediction, most features did not show any significant difference in performance after harmonization in KRAS prediction. Average AUCs of all multivariate predictive models for both EGFR and KRAS were significantly improved (q-value < 0.05) following ComBat harmonization. The mean ranges of AUCs increased following harmonization from 0.87–0.90 to 0.92–0.94 for EGFR, and from 0.85–0.90 to 0.91–0.94 for KRAS. The highest performance was achieved by harmonized F_R0.66_W0.75 model with AUC of 0.94, and 0.93 for EGFR and KRAS, respectively.
Our results demonstrated that regarding univariate modelling, while ComBat harmonization had generally a better impact on features for EGFR compared to KRAS status prediction, its effect is feature-dependent. Hence, no systematic effect was observed. Regarding the multivariate models, ComBat harmonization significantly improved the performance of all radiomics models toward more successful prediction of EGFR and KRAS mutation statuses in lung cancer patients. Thus, by eliminating the batch effect in multi-centric radiomic feature sets, harmonization is a promising tool for developing robust and reproducible radiomics using vast and variant datasets.
Display omitted
•We examine the impact of harmonization on multimodality PET/CT radiomics for EGFR and KRAS mutation prediction in NSCLC.•Harmonization had overall a better impact on features for EGFR prediction compared to KRAS, in univariate analysis.•Harmonization effect is feature dependent without a systematic effect on performance, in univariate analysis.•Harmonization significantly improved the performance of multivariate models for EGFR and KRAS mutation prediction.•Harmonization is promising for batch-effect elimination, for robust and reproducible biological status capturing.
The aim of this work is to investigate the applicability of radiomic features alone and in combination with clinical information for the prediction of renal cell carcinoma (RCC) patients’ overall ...survival after partial or radical nephrectomy. Clinical studies of 210 RCC patients from The Cancer Imaging Archive (TCIA) who underwent either partial or radical nephrectomy were included in this study. Regions of interest (ROIs) were manually defined on CT images. A total of 225 radiomic features were extracted and analyzed along with the 59 clinical features. An elastic net penalized Cox regression was used for feature selection. Accelerated failure time (AFT) with the shared frailty model was used to determine the effects of the selected features on the overall survival time. Eleven radiomic and twelve clinical features were selected based on their non-zero coefficients. Tumor grade, tumor malignancy, and pathology t-stage were the most significant predictors of overall survival (OS) among the clinical features (
p
< 0.002, < 0.02, and < 0.018, respectively). The most significant predictors of OS among the selected radiomic features were flatness, area density, and median (
p
< 0.02, < 0.02, and < 0.05, respectively). Along with important clinical features, such as tumor heterogeneity and tumor grade, imaging biomarkers such as tumor flatness, area density, and median are significantly correlated with OS of RCC patients.
Microfluidics has wide applications in different technologies such as biomedical engineering, chemistry engineering, and medicine. Generating droplets with desired size for special applications needs ...costly and time-consuming iterations due to the nonlinear behavior of multiphase flow in a microfluidic device and the effect of several parameters on it. Hence, designing a flexible way to predict the droplet size is necessary. In this paper, we use the Adaptive Neural Fuzzy Inference System (ANFIS), by mixing the artificial neural network (ANN) and fuzzy inference system (FIS), to study the parameters which have effects on droplet size. The four main dimensionless parameters, i.e. the Capillary number, the Reynolds number, the flow ratio and the viscosity ratio are regarded as the inputs and the droplet diameter as the output of the ANFIS. Using dimensionless groups cause to extract more comprehensive results and avoiding more experimental tests. With the ANFIS, droplet sizes could be predicted with the coefficient of determination of 0.92.
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