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During the unprecedented COVID-19 city lockdown, a unique opportunity arose to dissect the intricate dynamics of urban air quality, focusing on ultrafine particles (UFPs) and volatile organic compounds (VOCs). This study delves into the nuanced interplay between traffic patterns and UFP emissions in a subtropical urban setting during the spring-summer transition of 2021. Leveraging meticulous roadside measurements near a traffic nexus, our investigation unravels the intricate relationship between particle number size distribution (PNSD), VOCs mixing ratios, and detailed vehicle activity metrics. The soft lockdown era, marked by a 20–27% dip in overall traffic yet a surprising surge in early morning motorcycle activity, presented a natural experiment. We observed a consequential shift in the urban aerosol regime: the decrease in primary emissions from traffic substantially amplified the role of aged particles and secondary aerosols. This shift was particularly pronounced under stagnant atmospheric conditions, where reduced dilution exacerbated the influence of alternative emission sources, notably solvent evaporation, and was further accentuated with the resumption of normal traffic flows. A distinct seasonal trend emerged as warmer months approached, with aromatic VOCs such as toluene, ethylbenzene, and xylene not only increasing but also significantly contributing to more frequent particle growth events. These findings spotlight the criticality of targeted strategies at traffic hotspots, especially during periods susceptible to weak atmospheric dilution, to curb UFP and precursor emissions effectively. As we stand at the cusp of widespread vehicle electrification, this study underscores the imperative of a holistic approach to urban air quality management, embracing the complexities of primary emission reductions and the resultant shifts in atmospheric chemistry.

It is challenging to construct lateral homostructures with controllable geometry and repeated alternating configurations. Here the authors develop a generic approach for fabricating twisted lateral homostructures with tunable crystal orientation, epitaxial constrain, and phase stability.

The trend in history education is gradually emphasizing the development of historical thinking and collaborative problem-solving skills, which are expected to enhance the breadth and depth of learners' thinking. The integration of game-based learning with collaborative problem-solving activities designed for historical thinking is expected to help increase learners' motivation. Cognitive scaffolding can provide immediate guidance in educational games to facilitate proper understanding and discussion of historical knowledge among learners. In this study, we used the history educational game "Void Broken 2.0" embedded with cognitive scaffolding to guide students to use their historical thinking skills in collaborative problem-solving tasks, and analyzed the behavioral patterns of the learners in using cognitive scaffolding and discussing historical thinking. The participants in this study were 158 high school students divided into an experimental group (game-based learning) and a control group (reading-based learning). The results showed that the learning activities of both groups contributed to the learning effectiveness, and there was no significant difference between the groups. As the game progressed, students in the experimental group actively utilized the assistance of the cognitive scaffolding. The need for immediate access to the cognitive scaffold's prompts did not increase significantly as the game progressed, and students with high prior knowledge were willing to share their understanding and incorporate more diverse types of information into the discussion. Low prior knowledge students' need for immediate access to the cognitive scaffolding hints increased significantly as the game progressed, and they were more willing to work with their peers to find information related to chronological reasoning, engage in repetitive discussions, and explore possible clues.

This paper presents an interdisciplinary study that combines natural language processing and psycholinguistics research. The latent Dirichlet allocation (LDA) model was used for semantic relatedness computation to enable an understanding of the mechanisms and processes through which humans encode and retrieve lexical units. To test the similarity of the output of the topic model and human word association, the “Time-limited Multiple Divergent Thinking Test of Word Associative Strategy” (TLM-DTTWAS) was used to collect data and conduct tests with three food-related stimulus words. A total of 101 subjects took the tests, producing 4,251 words. The empirical results were analyzed on two levels: (1) by the expert word association classification: taxonomic and script proposed by Ross and Murphy (1999); (2) followed by the associative hierarchy theory of Mednick(1962), to sort the vocabulary test results into two associative hierarchies, “steep” and “flat.” The analysis indicated that human word association displays randomness, as well as generalization and continuity. After the experimental text was passed through the LDA latent semantic model which demonstrated highly significant correlation. This was a whole new attempt to train a data science model to make inference and prediction of human concept association which could be very useful in teaching as well as commercial applications.

Parent-child reading critically affects children's learning and development, and dialog reading (DR) is a highly effective strategy for parent-child reading. This study employed the prompt, expand, evaluate, and repeat (PEER) DR strategy to develop a picture book incorporating augmented reality (AR) technology. AR information is extracted from the book using a mobile device to guide children through PEER-based reading. To determine the effectiveness of the AR picture book, a total of 56 preschool children participated in an experiment. The children were divided into an experimental group and a control group. The participants in the control group were accompanied by their parents, and they engaged in conventional parent-child reading; however, the participants in the experimental group engaged alone in AR-assisted reading. The results indicated that the experimental group achieved learning effectiveness significantly higher than that of the control group. A cycle of behavioral changes," scanning with a mobile device [right arrow] reading augmented information [right arrow] answering the questions on the mobile device [right arrow] scanning with a mobile device", was observed. This cycle indicates that child-mobile device-picture book interaction increased the children's learning effectiveness.

The vertical distribution of aerosols is important for accurate surface PM2.5 retrieval and initial modeling forecasts of air pollution, but the observation of aerosol profiles on the regional scale is usually limited. Therefore, in this study, an approach to aerosol extinction profile fitting is proposed to improve surface PM2.5 retrieval from satellite observations. Owing to the high similarity of the single-peak extinction profile in the distribution pattern, the log-normal distribution is explored for the fitting model based on a decadal dataset (3248 in total) from Micro Pulse LiDAR (MPL) measurements. The logarithmic mean, standard deviation, and the height of peak extinction near-surface (Mode) are manually derived as the references for model construction. Considering the seasonal impacts on the planetary boundary layer height (PBLH), Mode, and the height of the surface layer, the extinction profile is then constructed in terms of the planetary boundary layer height (PBLH) and the total column aerosol optical depth (AOD). A comparison between fitted profiles and in situ measurements showed a high level of consistency in terms of the correlation coefficient (0.8973) and root-mean-square error (0.0415). The satellite AOD is subsequently applied for three-dimensional aerosol extinction, and the good agreement of the extinction coefficient with the PM2.5 within the surface layer indicates the good performance of the proposed fitting approach and the potential of satellite observations for providing accurate PM2.5 data on a regional scale.

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.

This study proposes a framework that incorporates mobile peer assessment and augmented reality (AR) technology to enhance interaction and learning effectiveness. According to the framework, a mobile AR peer assessment system has been developed to facilitate students to improve work interpretation, frequently interact with peers, represent their thinking and reflect upon their own works anytime anywhere. Moreover, the mobile AR technology provides personalized and location-based adaptive contents that enable individual students to interact with the mixed reality environment and observe how works are possibly applied to the real world in the future. In a fundamental design course, students used the system to acquire sufficient information in indoor and outdoor situations and mark peers’ work accurately based on appropriate assessment criteria. The experimental results showed that the system really assisted students in acquiring useful information, proposing their viewpoints, and further fostering critical thinking skills and reflection. During the process of interviews, most students made positive responses and provided meaningful suggestions. The system allows students to concentrate on observing and understanding the relative explanation and representation of works within a combined real–virtual environment and apply appropriate assessment criteria that produce sufficient assessment results to mark peers’ works. Rich feedback can encourage students to reflect upon their own works and improve the quality of their works.

This study used an online virtual environment to create and develop a Chinese learning community. The purposes of research were (1) to enhance the Chinese learners’ oral Chinese communication skills and (2) to change the community members’ Chinese speaking and teaching behavior. This is an action research. The research tried to create a community in a virtual environment. The research results showed that (1) a virtual community can enhance learner’s Chinese competence, and (2) future Chinese teachers’ instructional and leading skills can be developed in a virtual community situation.

Collaborative learning methods which emphasize peer interaction have been widely applied to increase the intensity and effectiveness of EFL reading programs. However, simply grouping students heterogeneously and assigning them group goals does not guarantee that effective collaborative learning will ensue. The present research includes two studies. In Study One, the weaknesses of collaborative learning in a traditional EFL setting were observed. Then, in Study Two, a mobile-device-supported peer-assisted learning (MPAL) system was developed for the purpose of addressing the identified weaknesses. Two classes of twenty-six third grade students participated in the present research to examine the unique contribution of MPAL to collaborative EFL reading activities. The collaborative behavior of elementary EFL learners was videotaped and analyzed. Detailed analysis of the videotaped behavior indicated that MPAL helped improve collaboration in elementary school level EFL learners and promotes their reading motivation.

In this study, an approach in determining effective mixing weight of soot aggregates from dust–soot aerosols is proposed to improve the accuracy of retrieving properties of polluted dusts by means of satellite remote sensing. Based on a pre-computed database containing several variables (such as wavelength, refractive index, soot mixing weight, surface reflectivity, observation geometries and aerosol optical depth (AOD)), the fan-shaped look-up tables can be drawn out accordingly for determining the mixing weights, AOD and single scattering albedo (SSA) of polluted dusts simultaneously with auxiliary regional dust properties and surface reflectivity. To validate the performance of the approach in this study, 6 cases study of polluted dusts (dust–soot aerosols) in Lower Egypt and Israel were examined with the ground-based measurements through AErosol RObotic NETwork (AERONET). The results show that the mean absolute differences could be reduced from 32.95% to 6.56% in AOD and from 2.67% to 0.83% in SSA retrievals for MODIS aerosol products when referenced to AERONET measurements, demonstrating the soundness of the proposed approach under different levels of dust loading, mixing weight and surface reflectivity. Furthermore, the developed algorithm is capable of providing the spatial distribution of the mixing weights and removing the requirement to assume that the dust plume properties are uniform. The case study further shows the spatially variant dust–soot mixing weight would improve the retrieval accuracy in AOD _mixture and SSA _mixture about 10.0% and 1.4% respectively.

This study was aimed at exploring how to use augmented reality (AR) technology to enhance the effect of performance assessment (PA). A mobile AR performance assessment system (MARPAS) was developed by integrating AR technology to reduce the limitations in observation and assessment during PA. This system includes three modules: Authentication, AR Context Aware, and AR Interaction. It identifies the character of system users, recognises subjects and activities that users should join in, and follows and assists users in showing their works and doing assessments anywhere anytime. Both qualitative and quantitative research were applied in this study, and it presents an action research conducted in PA institutions that, through a questionnaire, gave information in connection to the valuation of mobile AR technology and the achieved effects. The proposed mechanism of PA emphasises the approaches adopted to present student works and provides opportunities for enhancing student communication and interaction. In addition, the system enables students to explain their works and incorporate the feedback they receive into future work. More importantly, mobile AR can be applied to offer personalised features and appropriate information in particular areas. From the observation of survey results, students can interact with real or virtual information based on their needs. During this process, students can observe their own works from varying perspectives, acquire vital knowledge, develop the skills of critical thinking, and transform the process into a substantial self-established learning process.