The Effectiveness of Interactional Feedback on English Grammar Perception in Oral Context—Based on Chinese Students

Interactional Feedback is one crucial strategy that is widely implemented by language teachers in real classrooms, but there is no agreement about the effectiveness of different types of feedback on students learning. This study compared the different perceptions and different effectiveness of recasts and prompts on acquisition of past tense. Fifteen Chinese English learners and three language teachers were recruited from a university in the Midwest region of the U.S. Each teacher was responsible for helping five participants complete three tasks: the Interaction Task, the Retelling Task, and the Stimulated Recall Task. During the Interactional Task, each participant would complete three picture stories with the teacher under three conditions: recast, prompt and no feedback, respectively. Then, students were asked to retell the story immediately after the interaction with the teacher. Finally, the researcher of the study guided students to complete the Stimulated Recall Task to measure their perceptions of the different feedback conditions. After analyzing the Interactional Task and the Stimulated Recall Task, the results indicated that students were more successful in perceiving the target of recasts than prompts. For the effectiveness of the three conditions, the Friedman Test showed there was not a significant difference among recasts, prompts, and no feedback conditions that promoted participants to a better acquisition of the past tense. The findings of the study suggest the extent of effectiveness of feedback might be affected by variation of students. Therefore, ESL teachers should take students’ different backgrounds and learning experiences into account while choosing the personalized or tailored feedback for students

Comparison of feature engineering methods and classifiers for recognizing physical activity types in older adults using real-life IMU and GPS data

Physical Activities (PA) are crucial for human beings to stay healthy both physically and mentally. The physical activities of older adults show different characteristics than that of other age groups, such as lighter intensities and lower speeds. The MOASIS data is large-scale real-life mobility data collected from older adults in Switzerland. In this paper, IMU and GPS dimensions of MOASIS data are used to study the physical activity classification of the older population in real-life conditions. This paper focuses on feature engineering for machine learning methods, including feature calculation, feature extraction, and feature selection. First of all, this paper does a literature review of some of the papers under this theme, and summarizes the research gaps within this topic. The research gaps include: the application and comparison of dimension reduction and machine learning methods on such a real-life dataset focused on this specific age group, the application of GPS data for feature calculation in PA recognition, distinctive features extraction for PA types of older adults, the influence of validation methods on results of machine learning methods. Targeting the above research gaps, this paper puts forward three research questions: the comparison of different machine learning and dimension reduction methods, the comparison of the results of their application on this dataset, the impact of different dimensions of sensor data on the classification results. The results show that first, the most commonly used PCA feature extraction method can indeed improve the results of the KNN classifier in this data to a large extent, but it cannot help in improving the results of the unsupervised classifier Kmeans, which generally performs poorly in PA recognition. Second, Extra-tree performs best when considering the balance between time and accuracy among the classifiers compared. And the Recursive Feature Elimination method (RFECV) has the highest accuracy among the filter, wrapper and embedded feature selection methods based on the Extra-tree classifier. However, the differences in accuracy among the three methods are tiny. In addition, this paper concludes that the two validation methods compared (stratified k-fold validation and holdout validation) may affect the selection of hyper-parameters in model training. Finally, the feature importance ranking by different feature selection methods and the distinctive features for different PA types based on this dataset are also presented. For future studies, this paper suggests that more attention should be paid to the application of different sensor dimensions in PA recognition. Moreover, more fine hyperparameter adjustment of different models should be investigated

Photochemical Upcycling/Modification of Polystyrene-based Plastic Waste

The escalating accumulation of plastic waste in landfills and marine environments has become a pressing concern to society. Among all plastic-based waste, polystyrenes are widely utilized as a commodity plastic and present very low recyclability. To improve this scenario, photocatalysis has recently become one of the viable techniques which can be performed under mild conditions. In this concise review, we have highlighted recent advancements in the valorization of polystyrene-based plastic waste by mainly focusing on the selective functionalization of the C–H bonds. This strategy clearly holds strong promise for the sustainable and efficient conversion of polystyrene-based waste and contributes to the reduction of waste and resource conservation

Cognitive Behavior Therapy (CBT) sebagai Terapi Tingkat Kecemasan pada Lansia

This study aims to determine the effect of Cognitive Behavior Therapy (CBT) on anxiety levels in older people in Gumpang Village. This study used the pre-experimental method with the type of pre-post test research in one group (one-group pre-post test design). The results of the Wilcoxon Rank Test showed that the mean value before Cognitive Behavior Therapy (CBT) was 18.61. In contrast, the Wilcoxon signed ranks test showed hostile ranks or the difference in anxiety scores for older people in Gumpang Village was 87, meaning 87 elderly experienced decreased anxiety scores after Cognitive Behavior Therapy. In contrast, the mean rank or average decrease in anxiety is 44.00. Statistical test with the Wilcoxon Asymp sig test. (2-tailed) is 0.000 (p < 0.05). In conclusion, there is an effect of Cognitive Behavior Therapy on the level of anxiety in older people in Gumpang Village, to be precise at the Ngudi Waras Posyandu and the Panca Marga 4 Posyandu, Kartasura District, Sukoharjo Regency, Central Java Province.   Keywords: Cognitive Behavior Therapy, Anxiety, Elderl

Isolated Hepatic Splenosis: First Reported Case

Splenosis is the autotransplantation of splenic tissue, most commonly seen after traumatic splenic rupture and splenectomy. Post-traumatic splenosis is often considered a rare entity, but is probably underreported because of its asymptomatic nature. We describe the first reported case of splenosis presenting as a liver mass, indistinguishable from a liver tumor by standard preoperative evaluation. The pathophysiology, evaluation and management of splenosis is discussed as well as the decision to resect a benign appearing liver mass

A study on the matching of constraint between steam turbine blade and laboratory specimens

The matching of constraint between laboratory specimens and actual cracked structures is a key problem of the accurate structure integrity assessment. Different laboratory specimens and the steam turbine blade with different constraints were selected, the matching of constraint between steam turbine blade and laboratory specimens was investigated. The results shown that the steam turbine blade with 2c = 50 mm, a/2c = 0.20 has a matching constraint with single edge-notched bend specimen with a/W = 0.6 and single edge-notched tensile specimen with a/W = 0.3. The steam turbine blade with 2c = 50 mm, a/2c = 0.25 has a matching constraint with single edge-notched bend specimen with a/W = 0.7. The steam turbine blade with 2c = 50 mm, a/2c = 0.30 has a matching constraint with single edge-notched bend specimen with a/W = 0.5 and single edge-notched tensile specimen with a/W = 0.1. The steam turbine blade with 2c = 50 mm, a/2c = 0.35 has a matching constraint with single edge-notched bend specimen with a/W = 0.4, compact tension specimen with a/W = 0.3 and central-cracked tension specimen with a/W = 0.7. The steam turbine blade with a = 15 mm, a/2c = 0.30 has a matching constraint with compact tension specimen with a/W = 0.7 and single edge-notched tensile specimen with a/W = 0.5. The steam turbine blade with a = 15 mm, a/2c = 0.40 has a matching constraint with compact tension specimen with a/W = 0.4. The steam turbine blade with a = 15 mm, a/2c = 0.50 has a matching constraint with single edge-notched bend specimen with a/W = 0.5

Bayesian Inference of Phenotypic Plasticity of Cancer Cells Based on Dynamic Model for Temporal Cell Proportion Data

Mounting evidence underscores the prevalent hierarchical organization of cancer tissues. At the foundation of this hierarchy reside cancer stem cells, a subset of cells endowed with the pivotal role of engendering the entire cancer tissue through cell differentiation. In recent times, substantial attention has been directed towards the phenomenon of cancer cell plasticity, where the dynamic interconversion between cancer stem cells and non-stem cancer cells has garnered significant interest. Since the task of detecting cancer cell plasticity from empirical data remains a formidable challenge, we propose a Bayesian statistical framework designed to infer phenotypic plasticity within cancer cells, utilizing temporal data on cancer stem cell proportions. Our approach is grounded in a stochastic model, adept at capturing the dynamic behaviors of cells. Leveraging Bayesian analysis, we explore the moment equation governing cancer stem cell proportions, derived from the Kolmogorov forward equation of our stochastic model. With improved Euler method for ordinary differential equations, a new statistical method for parameter estimation in nonlinear ordinary differential equations models is developed, which also provides novel ideas for the study of compositional data. Extensive simulations robustly validate the efficacy of our proposed method. To further corroborate our findings, we apply our approach to analyze published data from SW620 colon cancer cell lines. Our results harmonize with \emph{in situ} experiments, thereby reinforcing the utility of our method in discerning and quantifying phenotypic plasticity within cancer cells