The aim of this study is to construct and validate “physical literacy self-assessment questionnaire” (PLAQ) for Chinese students in grades 3–6. This study uses qualitative and quantitative methods to ...construct evaluation indicators of PL and determine the weights of each indicator. The 60 items of original PLAQ was based on literature review and interviews, and administered to 1179 primary students graded 3–6 in China. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are used to optimize the structure and verify the reliability and validity of the questionnaire. The model of PLAQ is composed of 4 first-level indicators, 10 second-level indicators and 35 third-level indicators. The results of EFA and CFA resulted in a 44-items, 4-factor questionnaire. EFA item loadings ranged from 0.558 to 0.896, and Cronbach’s alpha ranged from 0.818 to 0.892. The results of CFA show that the constructed model fits well, and PLAQ has good convergent validity and discriminative validity. The PLAQ appeared to be reliable and valid that can be used as an assessment tool for students in grades 3–6. PLAQ can be used as a guide for the development of PL. Additionally, PLAQ gives us a shared understanding about what PL is and how it can be developed by Chinese children. However, studies on the accuracy and generalizability of the PLAQ should be conducted to improve it in the future.
Simultaneous localization and mapping (SLAM) methods based on an RGB-D camera have been studied and used in robot navigation and perception. So far, most such SLAM methods have been applied to a ...static environment. However, these methods are incapable of avoiding the drift errors caused by moving objects such as pedestrians, which limits their practical performance in real-world applications. In this paper, a new RGB-D SLAM with moving object detection for dynamic indoor scenes is proposed. The proposed detection method for moving objects is based on mathematical models and geometric constraints, and it can be incorporated into the SLAM process as a data filtering process. In order to verify the proposed method, we conducted sufficient experiments on the public TUM RGB-D dataset and a sequence image dataset from our Kinect V1 camera; both were acquired in common dynamic indoor scenes. The detailed experimental results of our improved RGB-D SLAM were summarized and demonstrate its effectiveness in dynamic indoor scenes.
Deep Transfer Learning (DTL) signifies a novel paradigm in machine learning, merging the superiorities of deep learning in feature representation with the merits of transfer learning in knowledge ...transference. This synergistic integration propels DTL to the forefront of research and development within the Intelligent Fault Diagnosis (IFD) sphere. While the early DTL paradigms, reliant on fine-tuning, demonstrated effectiveness, they encountered considerable obstacles in complex domains. In response to these challenges, Adversarial Deep Transfer Learning (ADTL) emerged. This review first categorizes ADTL into non-generative and generative models. The former expands upon traditional DTL, focusing on the efficient transference of features and mapping relationships, while the latter employs technologies such as Generative Adversarial Networks (GANs) to facilitate feature transformation. A thorough examination of the recent advancements of ADTL in the IFD field follows. The review concludes by summarizing the current challenges and future directions for DTL in fault diagnosis, including issues such as data imbalance, negative transfer, and adversarial training stability. Through this cohesive analysis, this review aims to offer valuable insights and guidance for the optimization and implementation of ADTL in real-world industrial scenarios.
Unpredictable drought–heatwave events occur frequently worldwide, causing low water availability (drought) and high temperatures (hot), with consequences for forest decline and mortality. Our ...knowledge of the potential instantaneous reactions and subsequent recovery of water-related physiological processes and vegetation indices in hot drought events remains unclear. Here, we investigated how the 2022 summer drought–heatwave event in the subtropical regions of China affected hydraulic traits and NDVI values in the forests of four common subtropical tree species. During the hot drought, the NDVI values of all four forests decreased (−31%~−23%), accompanied by leaf scorch and tree crown dieback. Among the four species, a hot drought event caused an instantaneous descent in hydraulic conductivity (Ks, −72%~−31%), stomatal conductance (gs, −94%~−50%), and midday water potential (−40%~−169%), with severe drought-induced stem xylem embolism. A trade-off was found between resistance and resilience in hot-drought-induced hydraulic dysfunction, as species with lower declines in Ks and gs during the hot drought had a shorter recovery in the post-stress phase. This study highlights that the 2022 hot drought event had severe negative instantaneous impacts on the forests of four subtropical tree species, which were reflected both in water-related physiological processes in the field and in remote sensing data from satellites.
In the study of indoor simultaneous localization and mapping (SLAM) problems using a stereo camera, two types of primary features-point and line segments-have been widely used to calculate the pose ...of the camera. However, many feature-based SLAM systems are not robust when the camera moves sharply or turns too quickly. In this paper, an improved indoor visual SLAM method to better utilize the advantages of point and line segment features and achieve robust results in difficult environments is proposed. First, point and line segment features are automatically extracted and matched to build two kinds of projection models. Subsequently, for the optimization problem of line segment features, we add minimization of angle observation in addition to the traditional re-projection error of endpoints. Finally, our model of motion estimation, which is adaptive to the motion state of the camera, is applied to build a new combinational Hessian matrix and gradient vector for iterated pose estimation. Furthermore, our proposal has been tested on EuRoC MAV datasets and sequence images captured with our stereo camera. The experimental results demonstrate the effectiveness of our improved point-line feature based visual SLAM method in improving localization accuracy when the camera moves with rapid rotation or violent fluctuation.
The primary objective of this study is to enhance the English language proficiency of undergraduate students pursuing bachelor's degree programs. This is achieved through the development of a ...mandatory English course based on Interest-driven creator (IDC) principles within a blended learning environment. The IDC course was implemented over a 16-week period, consisting of 16 lessons, at a private college in China. To evaluate the impact of this course, a quasi-experimental design was employed to analyse the language learning outcomes. The performance of two groups, the experimental group (EG) and the control group (CG), was assessed using the Exam English (standardized according to CEFR) as both a pretest and posttest measure. Additionally, a semi-structured interview was conducted to gather valuable insights into the students' learning experiences. The findings of this study indicate that the blended learning approach effectively improved the students' English proficiency in listening and speaking.
Existing optimization research on the crankshaft heat treatment process is mostly based on one-sided considerations, and less consideration is given to the matching of multiple process parameters, ...leading to irrational designs of heat treatment. To address this problem, this work investigates the influence mechanisms of cooling speed, tempering temperature, and holding time on the performance evaluation indexes of the straightness, residual stress, and martensite content of a crankshaft based on the response surface method. The results showed that the order of influence of these three different process parameters on the performance evaluation index was cooling speed > holding time > tempering temperature, and the order of influence on the performance evaluation indexes under multifactorial process parameters was cooling speed–holding time > cooling speed–tempering temperature > holding time–tempering temperature. The optimal process parameters were a cooling speed of 1.4 times the cooling oil, a tempering temperature of 555 °C, and a holding time of 6 h, with the straightness of the crankshaft reduced by 9.9%, the surface stress increased by 6.7%, and the martensitic content increased by 7.2% after the process optimization. This work can provide new clues for optimizing the heat treatment process parameters of crankshafts.
Ship fires are one of the main factors that endanger the safety of ships; because the ship is far away from land, the fire can be difficult to extinguish and could often cause huge losses. The engine ...room has many pieces of equipment and is the principal place of fire; however, due to its complex internal environment, it can bring many difficulties to the task of fire detection. The traditional detection methods have their own limitations, but fire detection using deep learning technology has the characteristics of high detection speed and accuracy. In this paper, we improve the YOLOv7-tiny model to enhance its detection performance. Firstly, partial convolution (PConv) and coordinate attention (CA) mechanisms are introduced into the model to improve its detection speed and feature extraction ability. Then, SIoU is used as a loss function to accelerate the model's convergence and improve accuracy. Finally, the experimental results on the dataset of the ship engine room fire made by us shows that the mAP@0.5 of the improved model is increased by 2.6%, and the speed is increased by 10 fps, which can meet the needs of engine room fire detection.
The composition and content of phenolic acids and flavonoids among the different varieties, development stages, and tissues of Chinese jujube (Ziziphus jujuba Mill.) were systematically examined ...using ultra-high-performance liquid chromatography to provide a reference for the evaluation and selection of high-value resources. Five key results were identified: (1) Overall, 13 different phenolic acids and flavonoids were detected from among the 20 excellent jujube varieties tested, of which 12 were from the fruits, 11 from the leaves, and 10 from the stems. Seven phenolic acids and flavonoids, including (+)-catechin, rutin, quercetin, luteolin, spinosin, gallic acid, and chlorogenic acid, were detected in all tissues. (2) The total and individual phenolic acids and flavonoids contents significantly decreased during fruit development in Ziziphus jujuba cv.Hupingzao. (3) The total phenolic acids and flavonoids content was the highest in the leaves of Ziziphus jujuba cv.Hupingzao, followed by the stems and fruits with significant differences among the content of these tissues. The main composition of the tissues also differed, with quercetin and rutin present in the leaves; (+)-catechin and rutin in the stems; and (+)-catechin, epicatechin, and rutin in the fruits. (4) The total content of phenolic acid and flavonoid ranged from 359.38 to 1041.33 mug/g FW across all examined varieties, with Ziziphus jujuba cv.Jishanbanzao having the highest content, and (+)-catechin as the main composition in all 20 varieties, followed by epicatechin, rutin, and quercetin. (5) Principal component analysis showed that (+)-catechin, epicatechin, gallic acid, and rutin contributed to the first two principal components for each variety. Together, these findings will assist with varietal selection when developing phenolic acids and f lavonoids functional products.
Vaccination is a valid strategy to prevent and control newly emerging and reemerging infectious diseases in humans and animals. However, synthetic and recombinant antigens are poor immunogenic to ...stimulate efficient and protective host immune response. Immunostimulants are indispensable factors of vaccines, which can promote to trigger fast, robust, and long-lasting immune responses. Importantly, immunotherapy with immunostimulants is increasing proved to be an effective and promising treatment of cancer, which could enhance the function of the immune system against tumor cells. Pattern recognition receptors (PRRs) play vital roles in inflammation and are central to innate and adaptive immune responses. Toll-like receptors (TLRs)-targeting immunostimulants have become one of the hotspots in adjuvant research and cancer therapy. Bacterial-origin immunoreactive molecules are usually the ligands of PRRs, which could be fast recognized by PRRs and activate immune response to eliminate pathogens. Varieties of bacterial immunoreactive molecules and bacterial component-mimicking molecules have been successfully used in vaccines and clinical therapy so far. This work provides a comprehensive review of the development, current state, mechanisms, and applications of bacterial-origin immunostimulants. The exploration of bacterial immunoreactive molecules, along with their corresponding mechanisms, holds immense significance in deepening our understanding of bacterial pathogenicity and in the development of promising immunostimulants.
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