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Collaborative Problem Solving (CPS) has received increasing attention for its role in promoting learners' cognitive and social development in STEM education. However, little is known about how learners interact dynamically within a group at different time granularities. This gap mainly resulted from overlooking the time dimension of interactions, leading to a lack of nuanced understanding of moment-to-moment interaction in CPS. In this study, we demonstrated the potential of "temporal group interaction density" in modeling online CPS interactions and investigated the impact of temporal interaction density on CPS processes and outcomes. Specifically, we proposed using cumulative weighted density to measure the holistic state of group interactions and explained the differences in group interactions with different collaborative performance and interaction densities by modeling the transition and evolution of interaction sequences through Apriori and cumulative relative centrality. Results indicated that group interaction density cannot directly predict their collaborative performance, but notable differences in interaction patterns existed in the high-performance groups with different interaction densities, while low-performance groups showed interactive commonalities towards the completion of CPS. The findings of this study guided the design of CPS interventions and supported the process mining of CPS interactions, with vital practical implications for CPS assessment and skills development.

This study aims to track college students' on-task rate during the teaching process and to analyze the influence of instructional strategies on on-task rate through the aspects of observable and internal engagement indicators. Thirty-six undergraduate students at a higher education institution in China participated in the study. Students' behaviors and their EEG signals were recorded from fifty-one learning activities. Analyses have been focused on identifying the determinants of student's engagement levels and revealing the impacts of behavioral sequences and cognitive sequences on student's engagement levels. The results show that: (1) instructional strategies, classroom behaviors, and cognitive states were significant predictors of students' on-task rate; (2) the continuity of classroom behaviors improved the on-task rate; and (3) the standard deviations of attention and cognitive load were positively correlated with the on-task rate. This study describes a case of integrating multimodal data analysis in classroom teaching and discusses practical implications for improving classroom teaching.

Blended learning, integrating online and in-person components, has been increasingly adopted in higher education to enhance students' learning experience and outcomes. While the advantages of blended learning are well-evidenced, research has primarily focused on the online pre-learning component, neglecting the significance of in-class activities. In-class activities play a crucial role in affording active learning opportunities (e.g., discussion, elaboration), necessitating a systemic understanding of their dynamics. The purpose of this study was thus to systemically investigate college students' learning behaviors during in-class activities in a blended course. In-class activities were video-recorded and labelled manually following a coding scheme. By establishing a linear regression model, the study identified listening to the instructor's lecture and taking notes as two predictors of students' learning gains. Additionally, sequential patterns of learning behaviors during in-class activities were examined. The reciprocal interactions between students' behavior of listening to the lecture and their note-taking actions were noted. The findings of this study contributed to a systemic view of blended learning by shedding light on students' learning behaviors and their implications for instructional practice.

Blended learning has been widely integrated in college-level computer science education. Despite evidence about benefits of blended learning, students' in-class activities remain underexplored. To afford effective blended learning experience, supporting students in both modalities is essential. This study thus took an initial step to fill the gap by investigating college students' in-class activities in a blended course from the perspective of attention. Using non-intrusive electroencephalography (EEG) instruments to collect attentional data, this study found students' attention in in-class activities positively correlated with their learning gains. Students' attention also varied across in-class activities, reaching a higher level in group discussions than in pre-tests and lectures. Linear regression analysis indicated students' length of time spent viewing online resources and their pre-test scores significantly predicted their in-class attention. The findings of the study provide insight into course design and facilitation for effective blended computer science courses.

In order to realize the real-time processing and analysis of astronomical ultra-wide bandwidth signals, this study proposes a sub-band division algorithm based on RFSoC. The algorithm uses Kaiser window to design FIR prototype low-pass filter, adopts critical sampling polyphase filter bank to decompose ultra-wide bandwidth signal into several sub-bands, and encapsulates each sub-band into VDIF data frame and sends it to GPU server. The algorithm is implemented on RFSoC platform, and its effectiveness is verified by simulation and actual observation. The experimental results show that the algorithm can divide the astronomical ultra-wide bandwidth signal into multiple sub-bands in real time, packetize and transmit them to GPU. This research provides reproducible design and project for ultra-wide bandwidth signal sub-band division with low spectrum leakage and aliasing, high data accuracy, and fast processing speed.

With the surge in astronomical data volume, modern astronomical research faces significant challenges in data storage, processing, and access. The I/O bottleneck issue in astronomical data processing is particularly prominent, limiting the efficiency of data processing. To address this issue, this paper proposes a tiered storage algorithm based on the access characteristics of astronomical data. The C4.5 decision tree algorithm is employed as the foundation to implement an astronomical data access correlation algorithm. Additionally, a data copy migration strategy is designed based on tiered storage technology to achieve efficient data access. Preprocessing tests were conducted on 418GB NSRT (Nanshan Radio Telescope) formaldehyde spectral line data, showcasing that tiered storage can potentially reduce data processing time by up to 38.15%. Similarly, utilizing 802.2 GB data from FAST (Five-hundred-meter Aperture Spherical radio Telescope) observations for pulsar search data processing tests, the tiered storage approach demonstrated a maximum reduction of 29.00% in data processing time. In concurrent testing of data processing workflows, the proposed astronomical data heat correlation algorithm in this paper achieved an average reduction of 17.78% in data processing time compared to centralized storage. Furthermore, in comparison to traditional heat algorithms, it reduced data processing time by 5.15%. The effectiveness of the proposed algorithm is positively correlated with the associativity between the algorithm and the processed data. The tiered storage algorithm based on the characteristics of astronomical data proposed in this paper is poised to provide algorithmic references for large-scale data processing in the field of astronomy in the future.

This paper reports a method to measure the mechanical properties of a single cell using a microfluidic chip with integrated force sensing and a liquid exchange function. A single cell is manipulated and positioned between a pushing probe and a force sensor probe using optical tweezers. These two on-chip probes were designed to capture and deform the cells. The single cell is deformed by moving the pushing probe, which is driven by an external force. The liquid–liquid interface is formed between the probes by laminar flow to change the extracellular environment. The position of the interface is shifted by controlling the injection pressure. Two positive pressures and one negative pressure are adjusted to balance the diffusion and perturbation of the flow. The mechanical properties of a single Synechocystis sp. strain PCC 6803 were measured in different osmotic concentration environments in the microfluidic chip. The liquid exchange was achieved in approximately 0.3–0.7 s, and the dynamic deformation of a single cell was revealed simultaneously. Measurements of two Young’s modulus values under alterable osmotic concentrations and the dynamic response of a single cell in osmotic shock can be collected within 30 s. Dynamic deformations of wild-type (WT) and mutant Synechocystis cells were investigated to reveal the functional mechanism of mechanosensitive (MS) channels. This system provides a novel method for monitoring the real-time mechanical dynamics of a single intact cell in response to rapid external osmotic changes; thus, it opens up novel opportunities for characterizing the accurate physiological function of MS channels in cells.

Real-time emotion recognition in conversations (ERC), which relies on only the historical utterances to achieve ERC, has recently gained increasing attention due to its significance in providing real-time empathetic services. Although utilizing multimodal information can mitigate the issues of unimodal approaches, few real-time ERC studies consider the differences in representation ability of different modalities and explore comprehensive conversational context from different perspectives based on different structures. Furthermore, the heavy annotation cost makes it difficult to collect sufficient labeled data, which also limits the performance of current supervised ERC approaches. To address these issues, we propose a novel framework SMFNM for real-time ERC, which integrates semi-supervised learning with multimodal fusion under the guidance of main-modal. Specifically, SMFNM utilizes additional unlabeled data to extract high-quality intra-modal representations, and implements cross-modal interaction to capture complementary information to enhance the audio representations. Then SMFNM employs the directed acyclic graph and the Gated Recurrent Units for exploring more accurate conversational context from both the multimodal and main-modal perspectives, respectively. Finally, these two types of contextual features are fused for emotion identification. Extensive experiments on benchmark datasets (i.e., IEMOCAP (4-way), IEMOCAP (6-way) and MELD) demonstrate the effectiveness, superiority and rationality of our SMFNM.

Band structure modulation may drive topological phase transitions, but tuning topological phases within the same material is challenging. Here, quantum oscillations are used to map the Berry phase and Dirac bandgap closing and reopening in a strain-induced topological insulator phase transition in ZrTe5.

Abstract The purpose of this study aimed to analyze the process of online collaborative problem solving (CPS) via brain-to-brain synchrony (BS) at the problem-understanding and problem-solving stages. Aiming to obtain additional insights than traditional approaches (survey and observation), BS refers to the synchronization of brain activity between two or more people, as an indicator of interpersonal interaction or common attention. Thirty-six undergraduate students participated. Results indicate the problem-understanding stage showed a higher level of BS than the problem-solving stage. Moreover, the level of BS at the problem-solving stage was significantly correlated with task performance. Groups with all high CPS skill students had the highest level of BS, while some of the mixed groups could achieve the same level of BS. BS is an effective indicator of CPS to group performance and individual interaction. Implications for the online CPS design and possible supports for the process of online CPS activity are also discussed.

Quiz question annotation aims to assign the most relevant knowledge point to a question, which is a key technology to support intelligent education applications. However, the existing methods only extract the explicit semantic information that reveals the literal meaning of a question, and ignore the implicit knowledge information that highlights the knowledge intention. To this end, an innovative dual-channel model, the Semantic-Knowledge Mapping Network (S-KMN) is proposed to enrich the question representation from two perspectives, semantic and knowledge, simultaneously. It integrates semantic features learning and knowledge mapping network (KMN) to extract explicit semantic features and implicit knowledge features of questions,respectively. Designing KMN to extract implicit knowledge features is the focus of this study. First, the context-aware and sequence information of knowledge attribute words in the question text is integrated into the knowledge attribute graph to form the knowledge representation of each question. Second, learning a projection matrix, which maps the knowledge representation to the latent knowledge space based on the scene base vectors, and the weighted summations of these base vectors serve as knowledge features. To enrich the question representation, an attention mechanism is introduced to fuse explicit semantic features and implicit knowledge features, which realizes further cognitive processing on the basis of understanding semantics. The experimental results on 19,410 real-world physics quiz questions in 30 knowledge points demonstrate that the S-KMN outperforms the state-of-the-art text classification-based question annotation method. Comprehensive analysis and ablation studies validate the superiority of our model in selecting knowledge-specific features.

IntroductionChina is one of the major producers and exporters of various kinds of citrus fruits. As one of China’s major citrus planting bases, Sichuan has a citrus planting area that exceeds 400,000 hectares. Meanwhile, citrus cultivation has become one of the important agricultural pillar industries in the region. Citrus fruits are reported to show various health-promoting effects, especially antioxidant activity. However, reports on the functional, nutritional and qualitative characteristics of different citrus varieties in Sichuan are still scarce.MethodsThe quality attributes (color parameters, shape, and size), juice properties (titratable acids and total soluble sugar), mineral elements, and health-promoting nutritional and functional components (protein, carbohydrates, fat, dietary fiber, ascorbic acid, phenolic acids, and flavonoids), as well as antioxidant properties of 10 typical citrus varieties cultivated in Sichuan, were systematically investigated and analyzed.Results and DiscussionSignificant differences among different citrus varieties were found. In particular, the total soluble sugar content of Mingrijian was higher than that of other citrus, suggesting its potential for fresh consumption and food processing. Moreover, a total of five flavonoids and nine phenolic acids were identified and quantified. Yuanhong, with higher contents of ascorbic acid and phenolic acids, was considered to be a valuable variety with excellent antioxidant capacity and can be used for value-added processing in the food industry. Principal component analysis and hierarchical cluster heatmap analysis suggested that there were significant differences among the 10 citrus varieties. Correlation analysis confirmed the significant contribution of ascorbic acid and phenolic acids to antioxidant capacity in citrus. The results can provide some references for the cultivation and selection of nutritious citrus fruits.

To examine the development of visual aesthetic sensitivity in students in China, 2,387 students from age 9 to age 22 (excluding ages 16–17) were tested by the Visual aesthetic Sensitivity Test-Revised. The development of visual aesthetic sensitivity across ages and genders, and the effect of artistic training on students’ visual aesthetic sensitivity were examined. The data of primary school and junior middle school students were collected by paper tests completed collectively in class, while the data of university students were collected by distributing and collecting online. Result suggests that students’ visual aesthetic sensitivity is relatively stable from age 9 to age 12 and increases at age 13. The visual aesthetic sensitivity of girls is significantly better than that of boys at age 15, 19, and 20 years of age. This study also found that artistic training improves students’ visual aesthetic sensitivity.

Tadpole swimming is a common swimming form in nature, but there are only few studies on the swimming properties of tadpoles and tadpole groups. This paper focuses on the control and energy consumption during tadpole swimming and studies the swimming formation suitable for the tadpole group based on their hydrodynamic properties. First, based on the immersed boundary-lattice Boltzmann method and tadpole model proposed in our previous studies, the swimming control strategy of tadpoles based on incremental proportional–integral–derivative control is proposed. Then, the influence of control quantity on tadpole swimming is analyzed, and the control thresholds are set to realize the stable swimming control of tadpoles. Furthermore, through energy analysis of the pairs of tadpoles at different lateral distances, phase differences, and horizontal distances, we found the reduction of tadpole lateral distance has a slight effect on energy-savings. It is the energy-efficient mode for swimming pairs of adjacent tadpoles, when the longitudinal distance is one body length and the phase difference is 5 π / 3. In addition, under different longitudinal distances, the phase difference for energy efficiency of tadpoles is also different, but it shows a periodicity. Finally, the study results were extended to a formation of three or four tadpoles for verification. Our research will provide a mechanical reference for studying tadpole swarm swimming and for the design and control of tadpole-like robots. [ABSTRACT FROM AUTHOR]

Thinning can improve soil nutrient supply, but the effects of thinning on soil phosphorus (P) contents and bioavailable mechanisms in high-density and short-rotation Eucalyptus coppice forests are not well reported. Therefore, we conducted five intensities of thinning treatments, which were 83% (283 tree ha−1, T1), 66% (566 tree ha−1, T2), 50% (833 tree ha−1, T3), 33% (1116 tree ha−1, T4), and 0% (1665 tree ha−1) in a 2nd 6-year-old E. grandis × E. urophylla coppice plantation with 8 years as a rotation, investigated soil nutrient contents, microbial biomass, and extracellular enzyme activities of 0–20 and 20–40 cm soil layers after two years of thinning, and analyzed the relationship between available phosphorus (AP) and other indicators. The results showed that soil total phosphorus (TP) contents in 2nd Eucalyptus coppice plantations were lower than in native forest ecosystems, but T1 significantly increased (p < 0.05) TP by 81.42% compared to CK of 0–20 cm, whereas T2 and T3 improved available phosphorus (AP) by 86.87%–212.86% compared to CK. However, soil organic carbon (SOC), dissolved organic carbon (DOC), total nitrogen (TN), and alkaline hydrolysable nitrogen (AN) were not significantly different (p < 0.05) among all treatments. According to the analysis, soil TP contents were significantly positively related (p < 0.001) to SOC; soil total nutrients and DOC contents had the highest standardized total effect on AP; meanwhile, the quotient of microbial biomass directly conducted soil AP contents. These results highlighted that thinning can be used to alleviate soil P shortages by promoting multinutrient and biological cycles in Eucalyptus coppice forests.

Agile-satellite mission planning is a crucial issue in the construction of satellite constellations. The large scale of remote sensing missions and the high complexity of constraints in agile-satellite mission planning pose challenges in the search for an optimal solution. To tackle the issue, a dynamic destroy deep-reinforcement learning (D3RL) model is designed to facilitate subsequent optimization operations via adaptive destruction to the existing solutions. Specifically, we first perform a clustering and embedding operation to reconstruct tasks into a clustering graph, thereby improving data utilization. Secondly, the D3RL model is established based on graph attention networks (GATs) to enhance the search efficiency for optimal solutions. Moreover, we present two applications of the D3RL model for intensive scenes: the deep-reinforcement learning (DRL) method and the D3RL-based large-neighborhood search method (DRL-LNS). Experimental simulation results illustrate that the D3RL-based approaches outperform the competition in terms of solutions’ quality and computational efficiency, particularly in more challenging large-scale scenarios. DRL-LNS outperforms ALNS with an average scheduling rate improvement of approximately 11% in Area instances. In contrast, the DRL approach performs better in World scenarios, with an average scheduling rate that is around 8% higher than that of ALNS.

Chiral BIC can reach ultrahigh quality factors (Q-factor) based on its asymmetry, with broken mirror symmetries and in-plane inversion. Only by in-plane structural perturbation can chiral quasi-BIC (q-BIC) appear, so it is much more realizable and reasonable for the manufacturers in practical productions and fabrications considering the technology and means that are available. In this paper, we design a new dielectric metasurface employing H-shaped silica meta-atoms in the lattice, which is symmetrical in structure, obtaining chiral BIC with ultrahigh Q-factor (exceeding 105). In this process, we change the length of the limbs of the structure to observe the specific BICs. Previous scholars have focused on near-infrared-wavelength bands, while we concentrate on the terahertz wavelength band (0.8–1 THz). We found that there is more than one BIC, thus realizing multiple BICs in the same structure; all of them exhibit excellent circular dichroism (CD) (the maximum value of CD is up to 0.8127) for reflectance and transmittance, which provides significant and unique guidance for the design of multi-sensors. Meanwhile, we performed temperature sensing with chiral BIC; the sensitivity for temperature sensing can reach 13.5 nm/°C, which exhibits high accuracy in measuring temperature. As a consequence, the result proposed in this study will make some contributions to advanced optical imaging, chiral sensors with high frequency and spectral resolution, optical monitoring of environmental water quality, multiple sensors, temperature sensing, biosensing, substance inspection and ambient monitoring and other relevant optical applications.

Compared to UV-driven catalysts, the utilization of sunlight to design efficient visible light catalysts remains much less explored. Herein, we prepared Fe3O4@ZnO@ZIF-8 three-component core-shell nanoparticles and investigate their corresponding photocatalytic degradation by using Rhodamine B (RhB) as a model contaminant under visible light. It is found that Fe3O4@ZnO@ZIF-8 demonstrates a significant enhancement in the photocatalytic degradation efficiency (98% of RhB was degraded within 100 min) with a reaction rate constant of 0.0292 min−1. These findings mark the significance of precise tailoring of core-shell Fe3O4@ZnO@ZIF-8 for achieving the desired degradation efficiency of organic pollutants under visible light.

Based on a composited Newton–Cotes formula, this paper proposes a numerical method to predict milling stability considering regenerative chatter and focusing on rate and prediction accuracy. First, the dynamic model of milling motion is expressed as state-space equations considering regenerative chatter, with the tooth passing period divided into a set of time intervals. Second, a composited Newton–Cotes formula is introduced to calculate the transition function map for each time interval. Third, the state transition matrix is constructed based on the above-mentioned transition function, and the prediction stability boundary is determined by the Floquet theory. Finally, simulation analysis and experimental verification are conducted to verify the effectiveness of the proposed method. The simulation results demonstrate that, for the milling model with a single degree of freedom (DOF), the convergence rate and prediction accuracy of the proposed method are higher than those of the comparison method. The experimental results demonstrate that, for the milling model with two DOFs, the machining parameters below the prediction stability boundary can avoid the chatter as much as possible, ensuring the machined surface quality.

The integration of liquid exchange and microfluidic chips plays a critical role in the biomedical and biophysical fields as it enables the control of the extracellular environment and allows for the simultaneous stimulation and detection of single cells. In this study, we present a novel approach for measuring the transient response of single cells using a system integrated with a microfluidic chip and a probe with a dual pump. The system was composed of a probe with a dual pump system, a microfluidic chip, optical tweezers, an external manipulator, an external piezo actuator, etc. Particularly, we incorporated the probe with the dual pump to allow for high-speed liquid change, and the localized flow control enabled a low disturbance contact force detection of single cells on the chip. Using this system, we measured the transient response of the cell swelling against the osmotic shock with a very fine time resolution. To demonstrate the concept, we first designed the double-barreled pipette, which was assembled with two piezo pumps to achieve a probe with the dual pump system, allowing for simultaneous liquid injection and suction. The microfluidic chip with on-chip probes was fabricated, and the integrated force sensor was calibrated. Second, we characterized the performance of the probe with the dual pump system, and the effect of the analysis position and area of the liquid exchange time was investigated. In addition, we optimized the applied injection voltage to achieve a complete concentration change, and the average liquid exchange time was achieved at approximately 3.33 ms. Finally, we demonstrated that the force sensor was only subjected to minor disturbances during the liquid exchange. This system was utilized to measure the deformation and the reactive force of Synechocystis sp. strain PCC 6803 in osmotic shock, with an average response time of approximately 16.33 ms. This system reveals the transient response of compressed single cells under millisecond osmotic shock which has the potential to characterize the accurate physiological function of ion channels.

Abstract As a result of mutations in the upstream components of the Wnt/β-catenin signaling pathway, this cascade is abnormally activated in colon cancer. Hence, identifying the activation mechanism of this pathway is an urgent need for the treatment of colon cancer. Here, we found an increase in ADCK1 (AarF domain-containing kinase 1) expression in clinical specimens of colon cancer and animal models. Upregulation of ADCK1 expression promoted the colony formation and infiltration of cancer cells. Downregulation of ADCK1 expression inhibited the colony formation and infiltration of cancer cells, in vivo tumorigenesis, migration, and organoid formation. Molecular mechanistic studies demonstrated that ADCK1 interacted with TCF4 (T-cell factor 4) to activate the β-catenin/TCF signaling pathway. In conclusion, our research revealed the functions of ADCK1 in the development of colon cancer and provided potential therapeutic targets.

The aim of the present study was to evaluate the effects of alternating red (660 nm) and blue (460 nm) light on the growth and nutritional quality of two-leaf-color pak choi (Brassica campestris L. ssp. chinensis var. communis). Four light treatments (supplemental alternating red and blue light with intervals of 0, 1, 2, and 4 hours, with a monochromatic light intensity of 100 μmol·m−2·s−1 and a cumulative lighting time of 16 hours per day) were conducted in a greenhouse under identical ambient light conditions (90 to 120 μmol·m−2·s−1 at 12:00 am) for 10 days before green- and red-leaf pak choi were harvested. The results showed that the two-leaf-color pak choi receiving alternating red and blue light exhibited more compact canopies and wider leaves than those under the control treatment, which was attributed to the shade avoidance syndrome of plants. The present study indicated that the biomass of green-leaf pak choi was much higher than that of red-leaf pak choi, but the nutritional quality of green-leaf pak choi was lower than that of red-leaf pak choi, and seemingly indicating that the regulation of metabolism for pak choi was species specific under light exposure. The trends of both biomass and the soluble sugar content were highest under the 1-hour treatment. The contents of chlorophyll a and total chlorophyll in both cultivars (green- and red-leaf pak choi) were significantly increased compared with control, without significant differences among the 1-, 2-, and 4-hour treatments, whereas chlorophyll b exhibited no significant difference in any treatment. Alternating red- and blue-light treatment significantly affected the carotenoid content, but different trends in green- and red-leaf pak choi were observed, with the highest contents being detected under the 1-hour and 4-hour treatments, respectively. With increasing time intervals, the highest soluble protein contents in two-leaf-color pak choi were observed in the 4-hour treatment, whereas nitrate contents were significantly decreased in the 4-hour treatment. Compared with 0 hours, the contents of vitamin C, phenolic compounds, flavonoids, and anthocyanins in two-leaf-color pak choi were significantly increased, but no significant differences were observed in vitamin C, phenolic compounds, and flavonoids among the 1-, 2-, and 4-hour treatments, similar to what was found for the anthocyanin content of green-leaf pak choi. However, the content of anthocyanins in red-leaf pak choi gradually increased with increasing time intervals, with the highest content being found in the 4-hour treatment. Supplemental alternating red and blue light slightly increased the antioxidant capacity [1,1-diphenyl-2-picrylhydrazyl (DPPH) radical scavenging rate and antioxidant power], but no significant differences were observed after 1, 2, and 4 hours of treatment. Taken together, treatment with an interval of 1 hour was the most effective for increasing the biomass of pak choi in this study, but treatment with a 4-hour interval should be considered to enhance the accumulation of health-promoting compounds.

The rapid development of learning technologies has enabled online learning paradigm to gain great popularity in both high education and K-12, which makes the prediction of student performance become one of the most popular research topics in education. However, the traditional prediction algorithms are originally designed for balanced dataset, while the educational dataset typically belongs to highly imbalanced dataset, which makes it more difficult to accurately identify the at-risk students. In order to solve this dilemma, this study proposes an integrated framework (LVAEPre) based on latent variational autoencoder (LVAE) with deep neural network (DNN) to alleviate the imbalanced distribution of educational dataset and further to provide early warning of at-risk students. Specifically, with the characteristics of educational data in mind, LVAE mainly aims to learn latent distribution of at-risk students and to generate at-risk samples for the purpose of obtaining a balanced dataset. DNN is to perform final performance prediction. Extensive experiments based on the collected K-12 dataset show that LVAEPre can effectively handle the imbalanced education dataset and provide much better and more stable prediction results than baseline methods in terms of accuracy and F1.5 score. The comparison of t-SNE visualization results further confirms the advantage of LVAE in dealing with imbalanced issue in educational dataset. Finally, through the identification of the significant predictors of LVAEPre in the experimental dataset, some suggestions for designing pedagogical interventions are put forward.

Here, a soft saddle-shaped dielectric elastomer actuator (SSDEA) is designed and a model-based tracking control approach is developed to achieve its desirable dynamics. In this actuator, a bent polyethylene terephthalate (PET) frame is connected with the dielectric elastomer (DE) driving mechanism. The DE driving mechanism induces PET frame elongation or shortening, which causes the movement of the SSDEA. With the benefit from its modular design, simple structure, and robust mechanical assembly, the SSDEA has the stable and linear driving capability. In order to describe the dynamic behaviors of this actuator, a hybrid modeling method that integrates a data-driven model with a physical model is presented, and a three-step identification method is proposed to obtain the model's parameters. Moreover, a model-based tracking control approach is further developed to achieve satisfactory control performance. Using experiments, the dynamics and control performance of the actuator are demonstrated effectively. [ABSTRACT FROM AUTHOR]

Previous studies have suggested that reading stories of role models can improve the growth mindsets of students. The current study aimed to investigate the types of stories that can increase the growth mindsets of high school students, undergraduates, and graduates and how many specific stories undergraduates with low and high perseverance need to read to improve their growth mindsets. In study 1, high school students, undergraduates, and graduates were assigned to read either five struggle stories or five achievement stories of role models. Their mindsets were measured before and after reading the stories. The results showed that reading struggle stories rather than achievement stories of role models increased the growth mindsets of undergraduates and graduates. In study 2, undergraduates with high and low perseverance were assigned to read five struggle stories or five achievement stories of role models. Their mindsets were measured before reading stories and after reading each story. The results showed that the growth mindsets of undergraduates with low perseverance increased after reading two struggle stories of role models, and increased further after reading five struggle stories of role models. More importantly, the level of growth mindsets of undergraduates with low perseverance was equal to that of undergraduates with high perseverance after reading five struggle stories of role models. These findings reveal that reading struggle stories of role models can improve the growth mindsets of undergraduates and graduates. The personality of students affects the effectiveness of story-based mindset intervention.

For a 2UPR-RPU over-constrained parallel manipulator, some geometric errors result in internal forces and deformations, which limit the improvement of the pose accuracy of the moving platform and shorten the service life of the manipulator. Analysis of these geometric errors is important for restricting them. In this study, an evaluation model is established to analyse the influence of geometric errors on the limbs’ comprehensive deformations for this manipulator. Firstly, the nominal inverse and actual forward kinematics are analysed according to the vector theory and the local product of the exponential formula. Secondly, the evaluation model of the limbs’ comprehensive deformations is established based on kinematics. Thirdly, 41 geometric errors causing internal forces and deformations are identified and the results are verified through simulations based on the evaluation model. Next, two global sensitivity indices are proposed and a sensitivity analysis is conducted using the Monte Carlo method throughout the reachable workspace of the manipulator. The results of the sensitivity analysis indicate that 10 geometric errors have no effects on the average angular comprehensive deformation and that the identified geometric errors have greater effects on the average linear comprehensive deformation. Therefore, the distribution of the global sensitivity index of the average linear comprehensive deformation is more meaningful for accuracy synthesis. Finally, simulations are performed to verify the results of sensitivity analysis.

Background: Licheng District of Jinan Prefecture reported a school-based varicella outbreak. We conducted an investigation to analyze the epidemiology and scope of the outbreak, determine varicella vaccine coverage on the school campus, and estimate varicella vaccine effectiveness (VE). Methods: In the epidemiological investigation, we determined the attack rate, the clinical manifestations of varicella cases, and histories of prior varicella disease and varicella vaccination. We tested students for presence of serum IgM antibodies, and we attempted to isolate the varicella virus from vesicular fluid samples. We used chi-square to compare incidences between classes and floors. VE was estimated using a retrospective cohort study. Results: There were 13 varicella cases in the outbreak. All were among fourth grade students - twelve in Class 7 and one in Class 6. The attack rate in the two classrooms was 14.3% (13/91). Clinical symptoms were rash (100%) and fever (46.15%). All cases were reported within one average incubation period, and the epidemic curve suggested common exposure. Six of the 13 cases previously received one dose of varicella vaccine with a median time between vaccination and infection of 9 years; the other seven cases had not been vaccinated. Varicella vaccine coverage with one or more doses was 81.31%; 2-dose coverage was 38.15%. The median age of receipt of dose 1 was 1.18 years, and median age for receiving dose 2 was 5.12 years. One-dose varicella VE was 73.2% (95% confidence interval: 37.0%, 88.6%), and two-dose VE was 100%. Conclusions: Varicella vaccine coverage has been gradually increasing in recent years, as ≥1-dose and 2-dose coverage rates are higher in younger children than older children. High one-dose vaccination coverage limited the outbreak scope and led to the breakthrough cases being mild. Mild cases were difficult to detect in a timely manner. Varicella vaccine was highly effective, with 1-dose VE of 73% nine years after vaccination and 2-dose VE of 100%. We strongly recommended that all school students receive two doses of varicella vaccine.