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This research investigated the applicability of translanguaging as a teaching strategy in China’s K-12 English learning context. Using a qualitative research method of systematic literature review informed by a social constructivist paradigm, data from 15 peer-reviewed articles were analyzed. The findings indicate that translanguaging serves as an effective pedagogical approach in K-12 EFL education. However, potential resistance from teachers and students may arise due to its novelty in the Chinese educational system. Implementing translanguaging can diminish rote learning, create a flexible linguistic environment, and address the identity challenges of bilingual or multilingual learners. The results suggest a paradigm shift in K-12 English education in China, advocating for translanguaging as an enriching instructional strategy.

Severe fever with thrombocytopenia syndrome virus (SFTSV), also known as the Dabie Banda virus, is an emerging tick-borne Bunyavirus that causes severe fever with thrombocytopenia syndrome (SFTS). Currently, symptomatic treatment and antiviral therapy with ribavirin and favipiravir are used in clinical management. However, their therapeutical efficacy is hardly satisfactory in patients with high viral load. In this study, we explored the antiviral effects of selective estrogen receptor modulators (SERMs) on SFTSV infection and the antiviral mechanisms of a representative SERM, bazedoxifene acetate (BZA). Our data show that SERMs potently inhibited SFTSV-induced cytopathic effect (CPE), the proliferation of infectious viral particles, and viral RNA replication and that BZA effectively protected mice from lethal viral challenge. The mode of action analysis reveals that BZA exerts antiviral effects during the post-entry stage of SFTSV infection. The transcriptome analysis reveals that GRASLND and CYP1A1 were upregulated, while TMEM45B and TXNIP were downregulated. Our findings suggest that SERMs have the potential to be used in the treatment of SFTSV infection. [ABSTRACT FROM AUTHOR]

Investigating the relationship between the months and traffic crashes is a foremost task for the safety improvement of mountainous freeways. Taking a mountainous freeway located in China as an example, this paper proposed a combined modeling framework to identify the relationships between months and different crash types. K-means and Apriori were initially used to extract the monthly distribution patterns of different types of crashes. A graphical approach and a risk calculation equation were developed to assess the output of K-means and Apriori. Then, using the assessment results as the input, a logistic regression model was constructed to quantify the effects of each month on crashes. The results indicate that the monthly distribution patterns of different crash types are inconsistent, i.e., for a specific month, the high risk of a certain crash type may be covered up if experts only focus on the total number of crashes. Moreover, when identified as high-risk months by K-means and Apriori, the crash-proneness will significantly increase several times than months identified as high-risk by only one of K-means and Apriori, thereby illustrating the superior performance of the mix-method. The conclusions can assist local relevant organizations in formulating strategies for preventing different types of traffic crashes in different months (e.g., the risk of rear-end crashes in August, the risk of fixed-object hitting crashes in February, and the risk of overturning crashes in October) and provide a methodological reference for relevant studies in other regions., Author(s): Changjian Zhang [1]; Jie He [1]; Chunguang Bai (corresponding author) [1]; Xintong Yan [1]; Chenwei Wang [1]; Yazhong Guo [2] 1. Introduction According to the World Health Organization (WHO), [...]

Vessel big data play a significant role in understanding vessel behaviors and thus facilitating the prosperity of waterway transportation. However, relevant research regarding vessel trajectory recognition in a broad range of narrow channels still lacks, especially using VITS data. The major objective of this paper is to conduct vessel trajectory analysis based on the novel VITS data and examine its availability in inland waterway vessel transportation. An alternate aim is to develop a more comprehensive framework to extract the vessel trajectory of multiple narrow waterways. This paper utilized vessel trajectory information of multiple narrow channels belonging to Yangtze River captured by VITS. Four compression algorithms were conducted. Additionally, the performances of three clustering approaches were evaluated. Speed distribution analysis was also implemented. The results indicated that slide window (SW) algorithm outperforms its other counterparts. Relative to DBSCAN, K-means and hierarchical clustering analysis (HCA) tend to be more capable of balanced classification. This paper is the first to utilize VITS data in vessel trajectory feature extraction and can potentially provide useful insight for vessel trajectory extraction in multiple narrow channels., Author(s): Xintong Yan [1]; Jie He [1]; Qiaoqiao Ren [1]; Chunguang Bai (corresponding author) [1]; Changjian Zhang [1]; Chenwei Wang [1] 1. Introduction Waterway transportation provides the most energy-efficient way [...]

Fillers can improve the tensile performance of ductile tubes. Mechanical analyses of tensile tubes with filler have generally focused on experimental and numerical studies on concrete-filled steel tube (CFST) components, while mechanical performance of tensile tubes with flexible supporting fillers has rarely been investigated. In the current research, we have proposed a “steel tube+pre-stressed flexible internal support” structure. Meanwhile, strengthening of tensile thin-walled tubes with internal supports was studied in terms of stress and deformation. The trends of yield strength and ultimate strength of tensile tubes were determined and calculation equations of yield strength and normal ultimate strength of tensile tubes with internal support were derived. Strengthening coefficient variations as functions of radius-thickness ratios of steel tubes and elastic moduli of internal supports as well the optimized internal support p corresponding to maximum increment of tensile performance of tubes were also determined. It was experimentally verified that the initial supporting pre-stress of internal support on steel tubes could be achieved by cold shrink fitting technique. Experimental results revealed that the developed composite structure significantly enhanced the mechanical performance, fracture toughness, and energy consumption characteristics of tensile tubes. Hence, the proposed structure was confirmed to have promising applications.

Despite the broad application of advanced fleet management systems (FMSs) in third-party logistics (3PL) companies, there is a marginally limited understanding of how to employ them to enhance transport energy efficiency. In a case study of a Chinese 3PL company, this paper analyzed data obtained from the online FMS to assess drivers' awareness of fuel-saving factors. A questionnaire was primarily designed to investigate the drivers' awareness of fuel-saving factors based on the reliability and validity test. Then, Extreme Gradient Boosting (XGBoost), a machine learning algorithm, was utilized to explore the intrinsic impacts of various factors on fuel consumption with the outputs providing the evaluation basis for individual awareness of the drivers. The results show a significant deviation in the driver's awareness of fuel-saving factors, among which the three indicators of engine speed, idling condition, and rolling without engine load are seriously underestimated, while the indicators related to the environment are seriously overestimated due to social expectations. In addition, the average speed was found to be the most important fuel-saving indicator besides the load. Based on these findings, this paper recommends that the 3PL companies choose a route with more freeways when planning, and the mileage should be controlled within 800km as far as possible., Author(s): Changjian Zhang [1]; Jie He [1]; Chunguang Bai (corresponding author) [1]; Xintong Yan [1]; Jian Gong [1]; Hao Zhang [1] 1. Introduction So far, fossil fuels remain the primary [...]

ETC and MTC lanes of China’s hybrid toll stations have various setting modes. When the vehicles passed through the toll stations, they would face a more complicated lane change process. If the ETC sign was set in the appropriate position in advance, the traffic safety in front of the toll stations would be effectively improved. The paper analyzed the process of lane change in 18 scenes by taking one-way three-lane highway at the upstream of toll station with six lanes as an example. Based on the definition of driver’s reaction distance, reading distance and safe distance of action, the theoretical calculation model of ETC sign preposition distance was established. It revealed the functional relationship between ETC lane layout schemes and sign preposition distance, and explored the reasonable setting position of the ETC sign in the full scenes of the lane layout. Finally, a case study of Nanjing toll station on Shanghai-Nanjing Expressway was carried out.

In order to cope with the growing environmental pollution triggered by traditional disposable express packing box, the design and promotion of recycling express packing boxes (REPB) is of necessity and feasibility. However, relevant research is still lacking. To address the aforementioned issues, this paper attempted to propose a comprehensive method for designing REPB based on five intelligent functions of REPB, i.e., information processing, transportation environmental state perception, transportation environment interaction, human-machine interaction, and cloud interaction. Additionally, to ensure the reliability and safety of REFB, this paper discussed the material and structure of REPB based on shock absorption, as well as the intelligent functions to protect REFB from being stolen and lost. This paper can aid in the establishment of framework for REPB design pertaining to intelligence, safety, feasibility and reliability. Future research could further explore the standardization of REFB.

The exit areas of urban freeways always experience serious traffic safety and congestion problems. As a basic task, vehicle trajectory data are difficult to extract by traditional manual counting method because of the complicated weaving flow and large traffic volume at the exit areas of urban freeways. This paper presents a novel vehicle trajectory extraction composite framework combining YOLOv4 vehicle detection algorithm, SORT vehicle tracking algorithm and KD-Tree trajectory data reconstruction algorithm (YSKT algorithms framework). An unmanned aerial vehicle (UAV) was used to collect traffic videos of urban freeways exit areas, and the YSKT algorithms framework was adopted to extract vehicle trajectory data from the collected traffic videos. According to the test results of 4 traffic video samples, around 95% of complete vehicle trajectories in the videos could be extracted. Furthermore, basic traffic flow characteristic parameters, traffic efficiency parameters and traffic safety parameters were calculated and analyzed according to the extracted vehicle trajectory data, which was expected to help researchers analyze traffic problems in this kind of road segment in future studies. [ABSTRACT FROM AUTHOR]