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Interaction in the classroom plays the key role for cultivating students’ 21st century skills. Insufficient breadth of interaction, uneven interaction opportunities, and chaotic interaction existed in many classrooms. With the integration of technology into education, many smart classrooms were built, with one of the aims being to promote interaction. However, the differences of interaction behaviors and engagement in a smart class versus a traditional class could rarely be found in literature, especially with the same teacher lecturing in both classes. In this study, a quasi-experiment was conducted by one experienced English teacher lecturing in a smart classroom with students and a traditional classroom with students for one semester. Research data were obtained by coding the 8 class videos with the proposed “Classroom Interaction Analysis Framework” and the adapted engagement questionnaire, and the data were analyzed using SPSS 24. Results showed that there were no significant differences in either interpersonal interaction or human–technology interaction; however students experienced significantly more engagement in the smart classroom. The reasons were analyzed and interaction patterns in smart classroom were discussed. Finally, a smart classroom interaction model was proposed to promote classroom interaction by considering the interplay of pedagogy, space, and technology.

Counterfeiting of banknotes remains a severe threat to economic security and social stability. The characterization of banknote has mainly relied on the assessment of various security features applied to the surface of the note. However, the surface features are easy to forge and contain insufficient information to discover the source. In this paper, a novel approach for banknote characterization has been proposed by employing spectral-domain optical coherence tomography (SD-OCT) that can provide structural and optical features. Three groups of counterfeit Chinese 100 Yuan banknotes produced by different printing manners and one group of authentic banknotes were examined by SD-OCT without any sample preparation and four distinct areas were selected for imaging. High-resolution tomographic and three-dimensional (3D) volumetric OCT images were obtained and a set of features were first revealed to characterize the banknotes qualitatively and quantitatively. The results demonstrated that SD-OCT was effective to detect and classify different types of counterfeit banknotes and could potentially be used to link counterfeit banknotes to their sources in a fast, simple and nondestructive manner.

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.

Determining the sequence of intersecting lines is a significant issue in the forensic document examination that can reveal the fraud or distinguish between different allegations. Optical coherence tomography (OCT) is a high-resolution cross-sectional imaging technique that has been introduced into forensic science field recently. The potential of OCT as a novel method to determine the sequence of intersecting lines was examined for the first time. In this study, a spectral-domain OCT system with a center wavelength of 900 nm was employed to perform nondestructive examination on determining the sequence of 18 heterogeneous intersecting line samples produced using three types of gel pens and three brands of stamp pad ink seals. Two-dimensional (2D) cross-sectional, and three-dimensional (3D) volumetric images of the intersecting lines were obtained by the OCT system. Several features were noted and analyzed to successfully determine the sequence of all the 18 samples. Blind tests were also conducted to demonstrate the effectiveness of OCT technique. The results illustrate that OCT technology can provide an effective and accurate method for sequencing intersecting lines of gel pen ink and seal ink, which may complement the conventional methods used in the examination of questioned documents.

Students’ active learning behavior determines learning performance. In post-COVID-19 period, Online Merging Offline (OMO) method become a common way of university students’ learning. However, at present, there are few studies in active learning behavior in the OMO mode. Combined with learning satisfaction and Technology Acceptance Model (TAM), this paper proposes an Online Active Learning (OAL) Model to predict the influencing factors of college students’ active learning behavior and then analyzes the differences between OMO model and pure online model by multi-group analysis (MGA) based on the model. The designed questionnaire was distributed, and a total of 498 valid questionnaires were collected. Using SmartPLS to analyze partial least squares structural equation modeling (PLS-SEM) and MGA, it is found that: (1) there are differences in the influencing factors of active learning between OMO and pure online model; the moderating effect of learning complaint in OMO mode is not established, and social isolation and age does not affect active learning in OMO mode; (2) learning quality, perceived ease of use, expectation, perceived usefulness, and social isolation indirectly affect active learning through learning satisfaction in both OMO model and pure online model; (3) learning satisfaction is an important mediating variable affecting active learning; and (4) learning complaints will negatively regulate the relationship between learning satisfaction and active learning only in pure online model. According to these findings, the paper provides theoretical and practical implementation suggestions implications for OMO teaching and OAL to ensure the expected learning outcome.

Game-based learning (GBL) can allow learners to acquire and construct knowledge in a fun and focused learning atmosphere. A systematic literature review of 42 papers from 2010 to 2020 in this study showed that the current difficulties in implementing GBL in classrooms could be classified into the following categories: infrastructure, resources, theoretical guidance, teacher’s capabilities and acceptance of GBL. In order to solve the above problems, the study constructs a technology enhanced GBL model, from the four parts of learning objective, learning process, learning evaluation, and smart classroom. In addition, this study adopted the Delphi method, inviting a total of 29 scholars, experts, teachers and school managers to explore how to implement GBL in smart classrooms. Finally, the technology enhanced GBL model was validated and the utilization approaches were provided at the conclusion part.

To study the impact of protection and hospital quarantine measure, government pre-warning mechanism and heterogeneity of underlying networks on epidemic spreading, a novel SEAIRS epidemic model is proposed on scale-free networks. The spreading dynamics of the model is studied by means of the mean-field theory. Two equilibriums and the basic reproductive number R0 of the model is analyzed in detail. The global asymptotic stability of the disease-free equilibrium, the permanence of the epidemic spreading and the global attractivity of the endemic equilibrium are proved. Sensitivity analysis shows that the basic reproductive number R0 is dependent on the coverage rate of home quarantine (ωQ,ηA ,ηS ), hospitalization rate η1 and government pre-warning intensity δ . Finally, the theoretical analysis results are confirmed by means of numerical simulations.

Considering the high use of inkjet printing in forgery cases, the classification of inkjet printing is particularly important in questioned document examination. In this work, a universal GC-MS method has been developed to analyze various ink components extracted from inkjet printouts. The results indicated that several components detected and identified across 195 inks could be used to distinguish printer manufacturers. A trend of decreasing solvent concentration over time was observed through the continuous monitoring of 7 samples. The results shown that this method is useful for forensic classification purposes, and can be useful regardless effects of storage environment, paper or printer. Furthermore, the application of this method in the analysis of counterfeit banknotes illustrated its feasibility and applicability.

Counterfeiting of banknotes is a crime and seriously harmful to economy. Examination of the paper, ink and toners used to make counterfeit banknotes can provide useful information to classify and link different cases in which the suspects use the same raw materials. In this paper, 21 paper samples of counterfeit banknotes seized from 13 cases were analyzed by wavelength dispersive X-ray fluorescence. After measuring the elemental composition in paper semi-quantitatively, the normalized weight percentage data of 10 elements were processed by multivariate statistical methods of cluster analysis and principle component analysis. All these paper samples were mainly classified into 3 groups. Nine separate cases were successfully linked. It is demonstrated that elemental composition measured by XRF is a useful way to compare and classify papers used in different cases.

Ultra high performance liquid chromatography-mass spectrometry (UHPLC-MS) technology and gas chromatography-mass spectrometry (GC-MS) technology were used to qualitatively analyze the major components in water based stamp pad inks including major colorants and volatile components. After the samples were supersonically extracted and then centrifuged, UHPLC-MS was used to separate and identify the major colorants. A ZORBAX Eclipse Plus Phenyl-Hexyl (50 mm x 4.6 mm, 1.8 microm) column and 15 mmol/L ammonium acetate-acetonitrile were utilized for the separation and negative selected ion monitoring mode (SIM) was set for the MS analysis. An HP-INNOWAX (30 m x 0.25 mm, 0.25 microm) column was employed in the GC-MS analysis with the full-scan mode to determine the volatiles. This study demonstrated that the major colorants in the inks and their imprints were Acid Red R, Eosin Y and Pigment Red 112; and the major volatiles were glycerol, 1,2-propanediol, etc. The method is rapid and accurate. It also demonstrates that the method can meet the requirements for imprint determination in material evidence identification. The work provides a reliable tool for the categorization research in the forensic sciences.