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With the rapid development of virtual technologies, there is a growing body of literature investigating the impact of virtual technologies on students’ spatial ability. However, it remains unclear whether virtual technologies can effectively improve students’ spatial ability. Therefore, this meta-analysis was conducted to synthesize the findings on the overall effects of virtual-based spatial ability enhancement. We systematically searched literature published from 2010 to 2020 (excluding non-empirical articles) and found 36 experimental peer-reviewed journal articles that met the inclusion criteria. Then, the random-effects model (REM) was used to calculate the pooled effect size. Results showed that virtual technologies have a medium effect on developing spatial ability with an overall effect size of 0.617. The studies were also coded to examine the moderating effects of their characteristics, such as learner stage, virtual technologies, disciplines, experimental design, learning application types, spatial ability, and testing instruments, on the outcome measure. The moderator analysis indicated that the virtual-based spatial ability improvement was more effective (a) for preschool learners, (b) in the fields of natural science and engineering technologies, (c) for all types of spatial ability, and (d) when learning during 3 to 6 months. Furthermore, augmented reality was most conducive to improving learners’ spatial ability compared with other virtual technologies. These findings provided insights for future studies and practices on using virtual technologies to cultivate spatial ability.

For its robust predictive power (compared to pure physics-based models) and sample-efficient training (compared to pure deep learning models), physics-informed deep learning (PIDL), a paradigm hybridizing physics-based models and deep neural networks (DNNs), has been booming in science and engineering fields. One key challenge of applying PIDL to various domains and problems lies in the design of a computational graph that integrates physics and DNNs. In other words, how the physics is encoded into DNNs and how the physics and data components are represented. In this paper, we offer an overview of a variety of architecture designs of PIDL computational graphs and how these structures are customized to traffic state estimation (TSE), a central problem in transportation engineering. When observation data, problem type, and goal vary, we demonstrate potential architectures of PIDL computational graphs and compare these variants using the same real-world dataset.

Predicting the future trajectories of multiple interacting pedestrians within a scene has increasingly gained importance in various fields, e.g., autonomous driving, human–robot interaction, and so on. The complexity of this problem is heightened due to the social dynamics among different pedestrians and their heterogeneous implicit preferences. In this paper, we present Information Maximizing Spatial-Temporal Graph Convolutional Attention Network (InfoSTGCAN), which takes into account both pedestrian interactions and heterogeneous behavior choice modeling. To effectively capture the complex interactions among pedestrians, we integrate spatial-temporal graph convolution and spatial-temporal graph attention. For grasping the heterogeneity in pedestrians’ behavior choices, our model goes a step further by learning to predict an individual-level latent code for each pedestrian. Each latent code represents a distinct pattern of movement choice. Finally, based on the observed historical trajectory and the learned latent code, the proposed method is trained to cover the ground-truth future trajectory of this pedestrian with a bi-variate Gaussian distribution. We evaluate the proposed method through a comprehensive list of experiments and demonstrate that our method outperforms all baseline methods on the commonly used metrics, Average Displacement Error and Final Displacement Error. Notably, visualizations of the generated trajectories reveal our method’s capacity to handle different scenarios.

Backgrounds and Purpose: The theory of “entero-pulmonary axis” proves that pneumonia leads to gut microbiota disturbance and Treg/Th17 immune imbalance. This study is aimed to explore the potential mechanism of fecal microbiota transplantation (FMT) in the treatment of Pseudomonas aeruginosa pneumonia, in order to provide new insights into the treatment of pneumonia.Methods: Pseudomonas aeruginosa and C57/BL6 mice were used to construct the acute pneumonia mouse model, and FMT was treated. Histopathological changes in lung and spleen were observed by HE staining. The expression of CD25, Foxp3 and IL-17 was observed by immunofluorescence. The proportion of Treg and Th17 cells was analyzed by flow cytometry. Serum IL-6, LPS, and IFN-γ levels were detected by ELISA. The expression of TNF-α, IFN-γ, IL-6, IL-2, Foxp3, IL-17, IL-10, and TGFβ1 in lung tissue homogenate was detected by qRT-PCR. 16S rRNA sequencing and non-targeted metabolomics were used to analyze gut microbiota and metabolism.Results: Pseudomonas aeruginosa caused the decrease of body weight, food and water intake, lung tissue, and spleen injury in mice with pneumonia. Meanwhile, it caused lung tissue and serum inflammation, and Treg/Th17 cell imbalance in mice with pneumonia. Pseudomonas aeruginosa reduced the diversity and number of gut microbiota in pneumonia mice, resulting in metabolic disorders, superpathway of quinolone and alkylquinolone biosynthesis. It also led to the decrease of 2-heptyl-3-hydroxy-4(1H)-quinolone biosynthesis, and the enrichment of Amino sugar and nucleotide sugar metabolism. FMT with or without antibiotic intervention restored gut microbiota abundance and diversity, suppressed inflammation and tissue damage, and promoted an immunological balance of Treg/Th17 cells in mice with pneumonia. In addition, FMT inhibited the aerobactin biosynthesis, 4-hydroxyphenylacetate degradation, superpathway of lipopolysaccharide biosynthesis and L-arabinose degradation IV function of microbiota, and improved amino sugar and nucleotide sugar metabolism.Conclusions: FMT restored the Treg/Th17 cells’ balance and improved inflammation and lung injury in mice with Pseudomonas aeruginosa pneumonia by regulating gut microbiota disturbance and metabolic disorder.

This study aimed to explore the effectiveness and safety of azvudine, nirmatrelvir/ritonavir, and molnupiravir in adult patients with mild-to-moderate COVID-19. This retrospective cohort study included patients with mild-to-moderate COVID-19 (asymptomatic, mild, and common types) at the First Hospital of Changsha (Hunan Province, China) between March and November 2022. Eligible patients were classified into the azvudine, nirmatrelvir/ritonavir, or molnupiravir groups according to the antiviral agents they received. The outcomes were the times to nucleic acid negative conversion (NANC). This study included 157 patients treated with azvudine (n = 66), molnupiravir (n = 66), or nirmatrelvir/ritonavir (n = 25). There were no statistically significant differences in the time from diagnosis to NANC among the azvudine, molnupiravir, and nirmatrelvir/ritonavir groups [median, 9 (95% CI 9–11) vs. 11 (95% CI 10–12) vs. 9 (95% CI 8–12) days, P  = 0.15], time from administration to NANC [median, 9 (95% CI 8–10) vs. 10 (95% CI 9.48–11) vs. 8.708 (95% CI 7.51–11) days, P  = 0.50], or hospital stay [median, 11 (95% CI 11–13) vs. 13 (95% CI 12–14) vs. 12 (95% CI 10–14) days, P  = 0.14], even after adjustment for sex, age, COVID-19 type, comorbidities, Ct level, time from diagnosis to antiviral treatment, and number of symptoms. The cumulative NANC rates in the azvudine, molnupiravir, and nirmatrelvir/ritonavir groups were 15.2%/12.3%/16.0% at day 5 ( P  = 0.858), 34.8%/21.5%/32.0% at day 7 ( P  = 0.226), 66.7%/52.3%/60.0% at 10 days ( P  = 0.246), and 86.4%/86.2%/80.0% at day 14 ( P  = 0.721). No serious adverse events were reported. Azvudine may be comparable to nirmatrelvir/ritonavir and molnupiravir in adult patients with mild-to-moderate COVID-19 regarding time to NANC, hospital stay, and AEs.

Stereocontrolled construction of tetrasubstituted olefins has been an attractive issue yet remains challenging for synthetic chemists. In this manuscript, alkynyl selenides, when treated with ArBCl 2 , are subject to an exclusive 1,1-carboboration, affording tetrasubstituted alkenes with excellent levels of E -selectivity. Detailed mechanistic studies, supported by DFT calculations, elucidates the role of selenium in this 1,1-addition process. Coupled with subsequent C-B and C-Se bond transformations, this 1,1-addition protocol constitutes a modular access to stereodefined all-carbon tetrasubstituted alkenes. The merit of this approach is demonstrated by programmed assembly of diverse functionalized multi-arylated alkenes, especially enabling the stereospecific synthesis of all six possible stereoisomers of tetraarylethene (TAE) derived from the random permutation of four distinct aryl substituents around the double bond. The diversity-oriented synthesis is further utilized to explore different TAE luminogenic properties and potential Se-containing antitumor lead compounds. Tetrasubstituted alkenes are found in bioactive compounds and materials, but modular stereospecific synthesis of these compounds remains challenging due to reactivity and selectivity issues of the intermediate compounds. Here, the authors disclose a carboboration of alkynyl selenides, which provides an intermediate with two readily distinguishable sites of functionalization, providing rapid access to stereodefined tetrasubstituted alkenes.

The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While the dataset provides a large amount of high-quality and multi-source driving information, people in academia are more interested in the underlying driving policy programmed in Waymo self-driving cars, which is inaccessible due to AV manufacturers’ proprietary protection. Accordingly, academic researchers have to make various assumptions to implement AV components in their models or simulations, which may not represent the realistic interactions in real-world traffic. Thus, this paper introduces an approach to learn a long short-term memory (LSTM)-based model for imitating the behavior of Waymo’s self-driving model. The proposed model has been evaluated based on Mean Absolute Error (MAE). The experimental results show that our model outperforms several baseline models in driving action prediction. In addition, a visualization tool is presented for verifying the performance of the model.

This paper aims to leverage Twitter data to understand travel mode choices during the pandemic. Tweets related to different travel modes in New York City (NYC) are fetched from Twitter in the two most recent years (January 2020–January 2022). Building on these data, we develop travel mode classifiers, adapted from natural language processing (NLP) models, to determine whether individual tweets are related to some travel mode (subway, bus, bike, taxi/Uber, and private vehicle). Sentiment analysis is performed to understand people’s attitudinal changes about mode choices during the pandemic. Results show that a majority of people had a positive attitude toward buses, bikes, and private vehicles, which is consistent with the phenomenon of many commuters shifting away from subways to buses, bikes and private vehicles during the pandemic. We analyze negative tweets related to travel modes and find that people were worried about those who did not wear masks on subways and buses. Based on users’ demographic information, we conduct regression analysis to analyze what factors affected people’s attitude toward public transit. We find that the attitude of users in the service industry was more easily affected by MTA subway service during the pandemic.

Autonomous vehicles (AV) hold great potential to increase road safety, reduce traffic congestion, and improve mobility systems. However, the deployment of AVs introduces new liability challenges when they are involved in car accidents. A new legal framework should be developed to tackle such a challenge. This paper proposes a legal framework, incorporating liability rules to rear-end crashes in mixed-traffic platoons with AVs and human-propelled vehicles (HV). We leverage a matrix game approach to understand interactions among players whose utility captures crash loss for drivers according to liability rules. We investigate how liability rules may impact the game equilibrium between vehicles and whether human drivers’ moral hazards arise if liability is not designed properly. We find that compared to the no-fault liability rule, contributory and comparative rules make road users have incentives to execute a smaller reaction time to improve road safety. There exists moral hazards for human drivers when risk-averse AV players are in the car platoon.

Mean-field games (MFGs) are developed to model the decision-making processes of a large number of interacting agents in multi-agent systems. This paper studies mean-field games on graphs (𝒢-MFGs). The equilibria of 𝒢-MFGs, namely, mean-field equilibria (MFE), are challenging to solve for their high-dimensional action space because each agent has to make decisions when they are at junction nodes or on edges. Furthermore, when the initial population state varies on graphs, we have to recompute MFE, which could be computationally challenging and memory-demanding. To improve the scalability and avoid repeatedly solving 𝒢-MFGs every time their initial state changes, this paper proposes physics-informed graph neural operators (PIGNO). The PIGNO utilizes a graph neural operator to generate population dynamics, given initial population distributions. To better train the neural operator, it leverages physics knowledge to propagate population state transitions on graphs. A learning algorithm is developed, and its performance is evaluated on autonomous driving games on road networks. Our results demonstrate that the PIGNO is scalable and generalizable when tested under unseen initial conditions.