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Chinese as a second language (CSL) learning has attracted more attention and supporting learners with adaptive resources becomes difficult. Some online systems recommended pre-designed resources from existing databases while the resources could not match learners' context. Designing resources dynamically according to learners' needs could be a solution while it's time-consuming. Targeting this problem, we proposed a "content-structure" loosely coupled model. Based on the model, we developed an automatic resource generation system and used it in a university. One class was chosen and the students were randomly assigned to the experimental (22 students) and control group (21 students). They participated in the course all the same except the resources generation method. During the learning process, the online behaviors were recorded for behavioral analysis and the learners' learning outcome and perceptions were measured by tests and questionnaires. Results showed that the system played positive roles in improving learners' learning outcome and perceptions. Moreover, we found that learners in experimental group participated more actively and there's evidence that the system could help learners better reflect on their needs. The results revealed the effectiveness of the system in supporting CSL learners' contextualized learning. This design will provide inspiration for future context-aware CSL learning research.

This paper explores what the measures of utterance fluency typically employed in Automatic Speech Evaluation (ASE), i.e. automated speaking assessments, tell us about oral proficiency. 60 Chinese learners of English completed the second part of the speaking section of IELTS and six tasks designed to measure the linguistic knowledge and processing assumed to underpin second language speech production. A sample of eight native speakers rated the learners' oral productions for functional adequacy. Analyses of the data confirm: (1) articulation rate, mid-clause pause frequency, and repetition frequency predict functional adequacy, (2) breadth of lexical knowledge is the main predictor of articulation rate as well as functional adequacy, (3) speed of syntactic processing predicts end-clause pause duration and speed of lexical processing predicts mid-clause pause duration, and (4) measures of utterance fluency together account for 60% of the variation in functional adequacy scores. These findings suggest that articulation rate best reflects overall functional adequacy. Moreover, other measures of utterance fluency reflect different areas of underlying knowledge and processing, opening up the possibility of automating diagnostic speaking tests.

This study examined the effects of using automatic speech recognition (ASR) technology on Chinese students' willingness to communicate (WTC) in oral English and the development trajectories of their interactional features in a flipped EFL context. One hundred sixty undergraduates from a Chinese university participated in the 14-week quasi-experiment. Both groups were taught in a flipped fashion. The treatment group was required to use the ASR technology for oral practice in their pre-class self-learning, while the control group conducted their self-learning without the ASR technology. The results found that the ASR-based oral practice led to a significant between-group difference in students' WTC with teacher and class and WTC with non-Chinese, showing that the ASR technology may contribute to improving the Chinese students' WTC in oral English. Conversely, except for the between-group effect on negotiation for meaning, there was no significant difference between the two groups on the other measures of interactional features. Moreover, none of the interactional features of the students in the treatment group changed significantly over time, indicating a limited role of the ASR technology on Chinese students' interactional features. Discussions were conducted regarding the contradictory effects of the ASR technology on WTC and peer interaction.

Through Many-Facet Rasch analysis, this study explores the rating differences between 1 computer automatic rater and 5 expert teacher raters on scoring 119 students in a computerized English listening-speaking test. Results indicate that both automatic and the teacher raters demonstrate good inter-rater reliability, though the automatic rater indicates less intra-rater reliability than college teacher and high school teacher raters under the stringent infit limits. There's no central tendency and random effect for both automatic and human raters. This research provides evidence for the automatic rating reform of the computerized English listening-speaking test (CELST) in Guangdong NMET and encourages the application of MFRM in actual score monitoring.

Although social networking apps and dictation-based automatic speech recognition (ASR) are now widely available in mobile phones, relatively little is known about whether and how these technological affordances can contribute to EFL pronunciation learning. The purpose of this study is to investigate the effectiveness of feedback from peers and/or ASR in mobile-assisted pronunciation learning. 84 Chinese EFL university students were assigned into three conditions, using WeChat (a multi-purpose mobile app) for autonomous ASR feedback (the Auto-ASR group), peer feedback (the Co-non-ASR group), or peer plus ASR feedback (the Co-ASR group). Quantitative data included the pronunciation pretest, posttest, and delayed posttest, and students' perception questionnaires, while qualitative data included students' interviews. The main findings are: (a) all three groups improved their pronunciation, but the Co-non-ASR and the Co-ASR groups outperformed the Auto-ASR group; (b) the three groups showed no significant difference in perception questionnaires; and (c) the interviews revealed some common and unique "technical," "social/psychological," and "educational" affordances and concerns about the three mobile-assisted learning conditions.

The development of artificial intelligence (AI) technology has enhanced the use of automated speech evaluation systems for language learners to practice speaking skills. This study investigated whether various automatic feedback offered by AI speech evaluation programs can help English as a foreign language (EFL) learners develop speaking skills. Forty EFL learners in China participated in this study. Data collection included qualitative and quantitative data. The results showed that the majority of participants believed they improved their speaking skills with the feedback offered by the AI speaking evaluation program. The findings also revealed that there were significant improvements in their mean scores of speaking skills in pre- and post-tests. Therefore, it is suggested that AI speaking evaluation systems could provide more varied textual feedback and practical suggestions to assist EFL learners in developing speaking skills.

In recent years, there has been a rapid growth of online learning in higher education. Apart from professional online course platforms, many online video sharing websites have also provided online learning opportunities for college students. One of the most popular websites among college students in China is Bilibili, a Shanghai-based Chinese video sharing website known for its danmaku commenting system. This system enables users to post scrolling comments synchronized with the video timeline while the video is playing. Which attracts young students due to the lively user interaction. As a result, an increasing number of Chinese students are utilizing online courses on Bilibili as a supplementary learning resource alongside traditional classroom learning. Despite its popularity, online learning faces the challenge of students' lack of participation more than traditional face-to-face learning does. To understand their learning involvement, we propose a novel danmaku-based automatic analysis model that extracts three dimensions of online learning engagement using the Text Mind software. This model enables us to understand the students' learning patterns both as clusters and as individuals. Based on the model results, we present corresponding intervention strategies for different types of students based on their individual engagement characteristics. [ABSTRACT FROM AUTHOR]

Bullying victimization is widely accepted to be associated with aggression. However, the mechanisms underlying this relationship remain unknown. To examine the long-term impact of bullying victimization on aggression, the present study tested the potential mediating effects of both anger rumination and hostile automatic thoughts. A total of 809 undergraduates from four universities across China (74.80% female; M age  = 19.63 years, SD = 0.82 years) completed the survey on three occasions, with a 6-month delay between Time 1 and Time 2 and a 1-year interval between Time 2 and Time 3. A cross-lagged model of anger rumination and hostile automatic thoughts was developed to test whether they predicted one another, and two structural models were constructed to test their mediating roles in bullying victimization and aggression. Findings indicated that anger rumination and hostile automatic thoughts are mutually predictable; the correlation between bullying victimization and aggression is mediated independently by anger rumination and hostile automatic thoughts, and the chain mediation of bullying victimization predicting aggression first through anger rumination and then through hostile automatic thinking was established. In addition, an alternative mediation model is also significant, with hostile automatic thoughts as the primary mediator and anger rumination as the secondary mediator. These results highlight the significance of anger rumination and hostile automatic thoughts in the long-term effects of bullying victimization on aggression. Interventions designed to reduce undergraduate students' anger rumination and hostile automatic thoughts may help reduce their aggression., (© 2024 The Authors. PsyCh Journal published by Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.)

Tuna resources are an important part of China's pelagic fishery production. However, for China's tuna fishery, tuna species caught at sea are still manually classified, which is a time-consuming and inefficient process; so China's tuna fishery needs to develop toward automation. This study uses gray-level co-occurrence matrix (GLCM) and VGG16 to visualize phenotypic texture through local images of three Thunnus species. At the same time, texture feature index data (TFD), deep feature data (DFD), and their combined feature data (CFD) are obtained from texture images. Support vector machine (SVM) with different kernel functions is used to classify phenotypic texture of tuna automatically. The study shows that visualized texture images of different tuna using GLCM and VGG16 have biological characteristics. In the classification results without cross-validation, the average classification accuracy of TFD in polynomial was 83%, the average classification accuracy of DFD in RBF (Radial basis function) was 93%, and the average classification accuracy of CFD in RBF was 95%. It is concluded that tuna phenotype texture can be efficiently classified by using SVM with different kernel functions. [ABSTRACT FROM AUTHOR]

Background: Early prediction of hematoma expansion (HE) is important for the development of therapeutic strategies for spontaneous intracerebral hemorrhage (sICH). Radiomics can help to predict early hematoma expansion in intracerebral hemorrhage. However, complex image processing procedures, especially hematoma segmentation, are time-consuming and dependent on assessor experience. We provide a fully automated hematoma segmentation method, and construct a hybrid predictive model for risk stratification of hematoma expansion., Purpose: To propose an automatic approach for predicting early hemorrhage expansion after spontaneous intracerebral hemorrhage using deep-learning and radiomics methods., Methods: A total of 258 patients with sICH were retrospectively enrolled for model construction and internal validation, while another two cohorts (n=87, 149) were employed for independent validation. For hemorrhage segmentation, an iterative segmentation procedure was performed to delineate the area using an nnU-Net framework. Radiomics models of intra-hemorrhage and multiscale peri-hemorrhage were established and evaluated, and the best discriminative-scale peri-hemorrhage radiomics model was selected for further analysis. Combining clinical factors and intra- and peri-hemorrhage radiomics signatures, a hybrid nomogram was constructed for the early HE prediction using multivariate logistic regression. For model validation, the receiver operating characteristic (ROC) curve analyses and DeLong test were used to evaluate the performances of the constructed models, and the calibration curve and decision curve analysis were performed for clinical application., Results: Our iterative auto-segmentation model showed satisfactory results for hematoma segmentation in all four cohorts. The Dice similarity coefficient of this hematoma segmentation model reached 0.90, showing an expert-level accuracy in hematoma segmentation. The consumed time of the efficient delineation was significantly decreased, from 18 min to less than 2 min, with the assistance of the auto-segmentation model. The radiomics model of 2-mm peri-hemorrhage had a preferable area under ROC curve (AUC) of 0.840 (95 % confidence interval [CI]: 0.768, 0.912) compared with the original (0-mm dilatation) model with an AUC of 0.796 (95 % CI: 0.717, 0.875). The clinical-radiomics hybrid model showed better performances for HE prediction, with AUC of 0.853, 0.852, 0.772, and 0.818 in the training, internal validation, and independent validation cohorts 1 and 2, respectively., Conclusions: The fully automatic clinical-radiomics model based on deep learning and radiomics exhibits a good ability for hematoma segmentation and a favorable performance in stratifying HE risks., Competing Interests: Declaration of competing interest All authors have completed the ICMJE uniform disclosure form. The authors have no conflicts of interest to declare., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

People are increasingly aware that metacognition can help us solve problems more effectively. Scholars believe that students' metacognition can be facilitated by promoting the interplay between the unlimited long work memory for holding schemas and limited work memory for processing ongoing activities. In this study, a workshop designed teacher mediation method between English language teachers and students is implemented that focuses on instructional talk supported by a visualized thinking framework in learning English grammar. Consequent to student's automation from retrieving schemas to engage in the self-regulatory process of English grammar problem-solving, this section involves three English language teachers and students in their taught classes in a secondary school in China. The teachers were equipped with an explicit approach to teacher mediation through researcher-designed workshops. As the teacher mediation approach was implemented in English language classrooms, the researchers made meaning of their experiences through the dialogue and interaction between teachers and students by revealing the essence in the mediation process: dialogic assessment, cognitive scheme construction, automated schema retrieval, and metacognitive schema construction. Through teachers' English pedagogical grammar practice in the three example cases, metacognitive teachers and learners are expected to be cultivated in an interdisciplinary view.

Language models have been increasingly popular for automatic creativity assessment, generating semantic distances to objectively measure the quality of creative ideas. However, there is currently a lack of an automatic assessment system for evaluating creative ideas in the Chinese language. To address this gap, we developed TransDis, a scoring system using transformer-based language models, capable of providing valid originality (novelty) and flexibility (variety) scores for Alternative Uses Task (AUT) responses in Chinese. Study 1 demonstrated that the latent model-rated originality factor, comprised of three transformer-based models, strongly predicted human originality ratings, and the model-rated flexibility strongly correlated with human flexibility ratings as well. Criterion validity analyses indicated that model-rated originality and flexibility positively correlated to other creativity measures, demonstrating similar validity to human ratings. Study 2 and 3 showed that TransDis effectively distinguished participants instructed to provide creative vs. common uses (Study 2) and participants instructed to generate ideas in a flexible vs. persistent way (Study 3). Our findings suggest that TransDis can be a reliable and low-cost tool for measuring idea originality and flexibility in Chinese language, potentially paving the way for automatic creativity assessment in other languages. We offer an open platform to compute originality and flexibility for AUT responses in Chinese and over 50 other languages ( https://osf.io/59jv2/ )., (© 2023. The Psychonomic Society, Inc.)

Background: Speech analysis has been expected to help as a screening tool for early detection of Alzheimer's disease (AD) and mild-cognitively impairment (MCI). Acoustic features and linguistic features are usually used in speech analysis. However, no studies have yet determined which type of features provides better screening effectiveness, especially in the large aging population of China., Objective: Firstly, to compare the screening effectiveness of acoustic features, linguistic features, and their combination using the same dataset. Secondly, to develop Chinese automated diagnosis model using self-collected natural discourse data obtained from native Chinese speakers., Methods: A total of 92 participants from communities in Shanghai, completed MoCA-B and a picture description task based on the Cookie Theft under the guidance of trained operators, and were divided into three groups including AD, MCI, and heathy control (HC) based on their MoCA-B score. Acoustic features (Pitches, Jitter, Shimmer, MFCCs, Formants) and linguistic features (part-of-speech, type-token ratio, information words, information units) are extracted. The machine algorithms used in this study included logistic regression, random forest (RF), support vector machines (SVM), Gaussian Naive Bayesian (GNB), and k-Nearest neighbor (kNN). The validation accuracies of the same ML model using acoustic features, linguistic features, and their combination were compared., Results: The accuracy with linguistic features is generally higher than acoustic features in training. The highest accuracy to differentiate HC and AD is 80.77% achieved by SVM, based on all the features extracted from the speech data, while the highest accuracy to differentiate HC and AD or MCI is 80.43% achieved by RF, based only on linguistic features., Conclusion: Our results suggest the utility and validity of linguistic features in the automated diagnosis of cognitive impairment, and validated the applicability of automated diagnosis for Chinese language data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Huang, Yang, Che and Yang.)

The PV/T-GSHP heating control system is proposed to address the energy consumption problem of building heating in northern China. By using TRNSYS to establish an automatic control model, the control of the heat pump start/stop, water supply temperature on the load side of the tank, and high temperature water return to the building can achieve the purpose of being energy saving, providing stable heating, and providing control of high temperature heating. The results show that the PV/T-GSHP control system has an annual fluctuation range of 0.01~0.17 kW in the heating period for a typical building heat supply and building heat load, and the system energy efficiency and energy consumption are increased by 14.5% and 33.91%, respectively, compared with the GSHP system, and the annual value of cost is reduced by 8.49% and 19.09% compared with the GSHP and traditional coal-fired boiler system, which shows that the PV/T-GSHP control system is more economical. It can be seen that the PV/T-GSHP control system has better heating stability, energy efficiency, and economy, and it provides theoretical significance for the current clean heating automatic control scheme in Chinese northern areas. [ABSTRACT FROM AUTHOR]

Seedling transplanting has been one of the most important planting steps for vegetable plug seedlings raising in China. However, manual and semi-automatic transplanting cannot fully meet the large-scale transplanting operations in recent years, due to the high labor cost. Alternatively, mechanization can also be a benefit to modern agriculture. An automatic seedling taking and throwing device can be expected for the vegetable plug seedlings to fully meet the vegetable transplanting for the high efficiency of vegetable plug seedling planting and the low labor intensity. Therefore, this study aims at the problems of the automatic seedling taking and throwing device for the vegetable plug seedling, such as the large structural size, easy failure in taking and throwing seedlings. The transplanting operation was proposed to analyze the individual taking and throwing seedlings, where the taking seedlings in a straight shape, and throwing seedlings in an arc shape. An arc-type expansion automatic seedling taking and throwing device was also designed for the transplanting operation of vegetable plug seedlings. A systematic analysis was performed on the main structure and working principle of this device. Three components included a seedling clamping assembly, a guided groove plate, and a rotary seedling receiving mechanism. Among them, the seedling clamping assembly was firstly bent into an arc-shaped expansion through the hinge structure, and then the eight vegetable plug seedlings were taken away from the seedling tray, and finally thrown into the seedling cups at one time. Theoretical analysis was carried out to determine the parameter equation of the guided groove centerline, including the radius of the circle formed by the geometric center of the seedling cups, and the minimum distance between two adjacent seedling cups. Some limitations were also considered, such as the reduced size of the rotary seedling receiving mechanism. As such, the mechanism was easy to assemble without the interference of seedling cups each other. A risk of collision was found between the vegetable plug seedling and the seedling cup when the seedling was thrown into the seedling cup with a high average frequency of taking and throwing seedlings. For this reason, the seedling throwing scheme was introduced to automatically set an appropriate delay using the current average frequency of taking and throwing seedlings. The centers of the substrate and the seedling cup coincided without deviation, in order to avoid substrates loss and seedling throwing failure caused by collision when the vegetable plug seedlings were thrown into the seedling cups under this seedling throwing scheme. The PLC automatic control system was built with the pneumatic circuits of the execution units, in order to coordinate the movements of various mechanisms, complete efficient and regular seedling taking and throwing operations. An orthogonal test was carried out, where the influencing factors, average frequency, air supply pressure, and average moisture content of substrates were selected as the experimental factors, while the success rate of taking and throwing seedlings and the rate of wounded seedlings were the target objectives. The range and variance analyses were performed on the experimental data. A better combination of experimental factors was obtained: the average frequency of taking and throwing seedlings was 90 plants per min, the air supply pressure was 0.4MPa, and the average moisture content of substrates was 30%. The demonstration tests were conducted with a combination of experimental factors. The success rate of taking and throwing seedlings was 94.05%, and the rate of wounded seedlings was 1.19%. This finding can provide a sound reference to design the automatic seedling taking and throwing device. [ABSTRACT FROM AUTHOR]

Background/Aim: Mitral regurgitation (MR) is frequently observed in non‐valvular atrial fibrillation (NVAF) patients and is a significant risk factor for its progression. This study aims to investigate the three‐dimensional anatomical characteristics of the mitral valve (MV) in patients with different types of NVAF to elucidate the underlying mechanisms of MR. Methods: A retrospective analysis was conducted on 82 paroxysmal atrial fibrillation patients (PAF group) and 66 persistent atrial fibrillation patients (PerAF group) who underwent real‐time three‐dimensional transesophageal echocardiography (RT 3D‐TEE) before initial ablation. Additionally, 30 patients undergoing RT 3D‐TEE for the assessment of patent foramen ovale were selected as the control group. Basic echocardiographic variables were measured, left atrioventricular volume and strain parameters were calculated using automated software (Dynamic‐HeartModel and AutoStrain). MV anatomic features were analyzed using the 4D‐MV Assessment software (TomTec Imaging Systems). Results: The maximum left atrial volume (LAVmax) was highest in the PerAF group, followed by the PAF and control groups, with statistically significant differences (p < 0.01). Left atrial reservoir strain (LASr) showed an opposite trend, being the lowest in the PerAF group (p < 0.01). The proportion of moderate to severe MR was significantly higher in the PerAF group (27.3%) compared to the PAF group (11.0%) (p < 0.01). Compared to controls, the AF groups exhibited mitral annular (MA) dilation, flattening of the MA plane, and increased leaflet area (all p < 0.05). Correlation analysis between effective regurgitant orifice area (EROA) and left atrioventricular structure and function parameters, as well as various annular parameters, showed that EROA was significantly correlated with left ventricular end‐diastolic volume (LVEDV), LAVmax, LASr, anterior and posterior diameter, annular area, ratio of annular height to commissural diameter (AH/CD ratio), posterior leaflet area, posterior leaflet length, and the C‐shaped annulus length (all p < 0.05). Conclusions: Significant remodeling of the MV apparatus occurs in NVAF patients, with more pronounced changes in PerAF patients, contributing to a higher incidence of moderate to severe MR. RT 3D‐TEE‐based MV automated assessment offers significant advantages in accurately diagnosing MV remodeling and supporting the evaluation of MR in NVAF patients. [ABSTRACT FROM AUTHOR]

With the advancement of automatic speech recognition (ASR) technology, ASR-based pronunciation assessment can diagnose learners' pronunciation problems. Meanwhile, ASR-based pronunciation training allows more opportunities for pronunciation practice. This study aims to investigate the effectiveness of ASR technology in diagnosing English pronunciation errors and to explore teachers' and learners' attitudes towards using ASR technology as a pronunciation assessment tool and as a learning tool. Five Chinese EFL learners participated in read-aloud tests, including a human-assessed test and an ASR-assessed test. Pronunciation error types diagnosed by the two tests were compared to determine the extent of overlapping areas. The findings demonstrate that there were overlaps between human rating and machine rating at the segmental level. Moreover, it was found that learners' varied pronunciation learning needs were met by using the ASR technology. Implications of the study will provide insights relevant to using ASR technology to facilitate English pronunciation assessment and learning.

The present study examined the effects of using automatic speech recognition (ASR) technology on oral complexity in a flipped English as a Foreign Language (EFL) course. A total of 160 undergraduates were enrolled in a 14-week quasi-experiment. The experimental group (EG) and the control group (CG) were taught with a flipped approach, but the EG students needed to undertake an additional pre-class task with ASR technology. In each unit, all students' in-class task performance was recorded, based on which the metrics of oral complexity were coded and computed. A two-way between- and within-subjects repeated measures design was conducted to examine the effects of the group factor, the time factor and the group × time interaction effects. The results showed that the EG students performed statistically better than their counterparts in the CG on lexical complexity and syntactic complexity. Moreover, significant improvement in phrasal complexity was witnessed over time in both groups. Significant group × time interaction effects were witnessed on overall complexity or subordination complexity. The gradients of the EG trajectories of the two metrics were greater than those of the CG. However, on phrasal complexity, the interaction effect was not significant.

Traditional educational giants and natural language processing companies have launched several artificial intelligence (AI)-enabled digital learning applications to facilitate language learning. One typical application of AI in digital language education is the automatic scoring application that provides feedback on pronunciation repeat outcomes. This research is motivated by the usage of automatic scoring-empowered digital learning tools by language learners, and set out to uncover the influencing mechanisms of AI-enabled automatic scoring application affordances on learners' continuous learning intention. Specifically, based on affordance theory, we found several automatic scoring application affordances through in-depth interviews. Considering the current lack of investigations on the mechanisms underlying automatic scoring application and its implications for learners' learning behaviors, we built a model to examine the role of automatic scoring application affordances on cognitive/emotional engagement and following continuous learning intention. We further examined the moderation role of in-job learners and student learners on the above relationships. The model was tested using a survey of 260 Chinese foreign language learners who used AI-empowered learning tools to facilitate their language learning practices. This study explores why learners continuously use AI-enabled automatic scoring applications by identifying the affordances that differentiate it from traditional educational technologies. Practitioners could take the identified affordances into account when designing AI-enabled language learning applications.

Due to benefits for teaching and learning, an increasing number of studies have focused on classroom dialogue and how to make it productive. Coding, in which the transcribed conversation is allocated to a set of features, is commonly employed to deal with the textual data arising from this dialogue. This is generally done manually and cannot provide timely feedback to the participants. To address this issue, we explored the possibility of automatically classifying the semantic content of classroom dialogue. Seven categories (prior-known knowledge, analysis, coordination, speculation, uptake, agreement and querying) were distinguished automatically using an artificial neural network-based model. The model achieved acceptable performance and was comparable to human coding. Information about quality of dialogue can be identified in a timely manner. With this knowledge, classroom dialogue can be managed more skilfully, and a more productive form of dialogue is likely to be achieved by teachers and students.

Background: Stroke is a prevalent disease with a significant global impact. Effective assessment of stroke severity is vital for an accurate diagnosis, appropriate treatment, and optimal clinical outcomes. The National Institutes of Health Stroke Scale (NIHSS) is a widely used scale for quantitatively assessing stroke severity. However, the current manual scoring of NIHSS is labor-intensive, time-consuming, and sometimes unreliable. Applying artificial intelligence (AI) techniques to automate the quantitative assessment of stroke on vast amounts of electronic health records (EHRs) has attracted much interest., Objective: This study aims to develop an automatic, quantitative stroke severity assessment framework through automating the entire NIHSS scoring process on Chinese clinical EHRs., Methods: Our approach consists of two major parts: Chinese clinical named entity recognition (CNER) with a domain-adaptive pre-trained large language model (LLM) and automated NIHSS scoring. To build a high-performing CNER model, we first construct a stroke-specific, densely annotated dataset "Chinese Stroke Clinical Records" (CSCR) from EHRs provided by our partner hospital, based on a stroke ontology that defines semantically related entities for stroke assessment. We then pre-train a Chinese clinical LLM coined "CliRoberta" through domain-adaptive transfer learning and construct a deep learning-based CNER model that can accurately extract entities directly from Chinese EHRs. Finally, an automated, end-to-end NIHSS scoring pipeline is proposed by mapping the extracted entities to relevant NIHSS items and values, to quantitatively assess the stroke severity., Results: Results obtained on a benchmark dataset CCKS2019 and our newly created CSCR dataset demonstrate the superior performance of our domain-adaptive pre-trained LLM and the CNER model, compared with the existing benchmark LLMs and CNER models. The high F1 score of 0.990 ensures the reliability of our model in accurately extracting the entities for the subsequent automatic NIHSS scoring. Subsequently, our automated, end-to-end NIHSS scoring approach achieved excellent inter-rater agreement (0.823) and intraclass consistency (0.986) with the ground truth and significantly reduced the processing time from minutes to a few seconds., Conclusion: Our proposed automatic and quantitative framework for assessing stroke severity demonstrates exceptional performance and reliability through directly scoring the NIHSS from diagnostic notes in Chinese clinical EHRs. Moreover, this study also contributes a new clinical dataset, a pre-trained clinical LLM, and an effective deep learning-based CNER model. The deployment of these advanced algorithms can improve the accuracy and efficiency of clinical assessment, and help improve the quality, affordability and productivity of healthcare services., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Purpose: Heart failure (HF) is a serious and complex syndrome with a high mortality rate. In clinical diagnosis, the correct classification of HF is helpful. In our previous work, we proposed a self-supervised learning framework of HF classification (SSLHF) on cine cardiac magnetic resonance images (Cine-CMR). However, this method lacks the integration of three dimensions of spatial information and temporal information. Thus, this study aims at proposing an automatic 4D HF classification algorithm., Methods: To construct a 4D classification model, we proposed an extensional framework called 4D-SSLHF. It mainly consists of self-supervised image restoration and HF classification. The image restoration proxy task utilizes three image transformation methods to enhance the exploration of spatial and temporal information in the Cine-CMR. In the classification task, we proposed a Siamese Conv-LSTM network by combining the Siamese network and bi-directional Conv-LSTM to integrate the features of the four dimensions simultaneously., Results: Experimental results on 184 patients from Shanghai Chest Hospital achieved an AUC of 0.8794 and an ACC of 0.8402 in the five-fold cross-validation. Compared with our previous work, the improvements in AUC and ACC were 2.89 % and 1.94 %, respectively., Conclusions: In this study, we proposed a novel self-supervised learning framework named 4D-SSLHF for HF classification based on Cine-CMR. The proposed 4D-SSLHF can mine 3D spatial information and temporal information in Cine-CMR images well and accurately classify different categories of HF. The good classification results show our method's potential to assist physicians in choosing personalized treatment., (© 2023. CARS.)

Background: Intestinal parasitic infections can harm health by causing malnutrition, anemia, impaired growth and cognitive development, and alterations in microbiota composition and immune responses. Therefore, it is crucial to examine stool samples to diagnose parasitic infections. However, the traditional microscopic detection method is time-consuming, labor-intensive, and dependent on the expertise and training of microscopists. Hence, there is a need for a low-complexity, high-throughput, and cost-effective alternative to labor-intensive microscopic examinations. Methods: This study aimed to compare the performance of a fully automatic digital feces analyzer, Orienter Model FA280 (People's Republic of China) with that of the formalin-ethyl acetate concentration technique (FECT). We assessed and compared the agreement between the FA280 and the FECT for parasite detection and species identification in stool samples. The first part of the study analyzed 200 fresh stool samples for parasite detection using the FECT and FA280. With the FA280, the automatic feces analyzer performed the testing, and the digital microscope images were uploaded and automatically evaluated using an artificial intelligence (AI) program. Additionally, a skilled medical technologist conducted a user audit of the FA280 findings. The second set of samples comprised 800 preserved stool samples (preserved in 10% formalin). These samples were examined for parasites using the FECT and FA280 with a user audit. Results: For the first set of stool samples, there was no statistically significant difference in the pairwise agreements between the FECT and the FA280 with a user audit (exact binomial test, P = 1). However, there were statistically significant differences between the pairwise agreements for the FECT and the FA280 with the AI report (McNemar's test, P < 0.001). The agreement for the species identification of parasites between the FA280 with AI report and FECT showed fair agreement (overall agreement = 75.5%, kappa [κ] = 0.367, 95% CI 0.248–0.486). On the other hand, the user audit for the FA280 and FECT showed perfect agreement (overall agreement = 100%, κ = 1.00, 95% CI 1.00–1.00). For the second set of samples, the FECT detected significantly more positive samples for parasites than the FA280 with a user audit (McNemar's test, P < 0.001). The disparity in results may be attributed to the FECT using significantly larger stool samples than those used by the FA280. The larger sample size used by the FECT potentially contributed to the higher parasite detection rate. Regarding species identification, there was strong agreement between the FECT and the FA280 with a user audit for helminths (κ = 0.857, 95% CI 0.82–0.894). Similarly, there was perfect agreement for the species identification of protozoa between the FECT and the FA280 with user audit (κ = 1.00, 95% CI 1.00–1.00). Conclusions: Although the FA280 has advantages in terms of simplicity, shorter performance time, and reduced contamination in the laboratory, there are some limitations to consider. These include a higher cost per sample testing and a lower sensitivity compared to the FECT. However, the FA280 enables rapid, convenient, and safe stool examination of parasitic infections. [ABSTRACT FROM AUTHOR]

The evolution of forest landscape patterns can reveal the landscape stability of forest dynamics undergoing complex ecological processes. Analysis of forest landscape dynamics in regions under ecological restoration can evaluate the impact of large-scale afforestation on habitat quality and provide a scientific basis for achieving sustainable eco-environment development. In this study, a method for assessing forest landscape stability by characterizing changes in forest landscape patterns was proposed. Toeplitz inverse covariance-based clustering (TICC) was used to automatically identify landscape pattern evolution by investigating the synergistic changes of two landscape indices—forest cover area (CA) and patch density (PD)—and to extract the short-term processes—degradation, restoration, and stable—that took place between 1987 and 2021. Four long-term evolution modes, no change, increase, decrease, and wave, based on the temporal distribution of short-term change processes, were also defined to assess landscape stability. Our results showed that (i) the forest's short-term change processes have various forms. The restoration subsequence was the largest and accounted for 46% of the total subsequence and existed in 75% of the landscape units. The time distribution of these three change processes showed that more landscape units have begun to transition into a stable state. (ii) The long-term change modes showed an aggregation distribution law and indicated that 57% of the landscape units were stable and 6.7% were unstable. Therefore, our study can provide a new perspective for the dynamic analysis of landscape patterns and offer insights for formulating better ecological restoration strategies. [ABSTRACT FROM AUTHOR]

Passive differential optical absorption spectroscopy (DOAS) is widely used to monitor the three-dimensional distribution of atmospheric pollutants. However, the observational and retrieval accuracy of this technique is significantly influenced by the precise wavelength calibration of solar spectra. Current calibration methods face challenges in automation when dealing with complex remote-sensing conditions. We introduce a novel automatic wavelength calibration algorithm for passive DOAS based on sequence-matching technology to estimate the spectral parameters of the spectrometer channels, integrating advanced processing measures such as feature structure enhancement and sub-pixel interpolation. These measures significantly reduce the dependency on reference spectrum resolution and accurately correct even minor spectral shifts. We perform sensitivity experiments using synthetic spectra to determine optimal retrieval configurations, followed by field tests at four cities on the Yangtze River Delta, China, to calibrate and compare passive DOAS instruments of various resolutions. Comparative verification in these field studies demonstrated that our algorithm was suitable for rapid spectral calibration within a wider resolution range of 0.03 nm to 0.1 nm with a wavelength inversion error < 0.01 nm. This highlights the applicability and calibration precision of our algorithm. [ABSTRACT FROM AUTHOR]

Airborne wind measurement is of great significance for understanding atmospheric motion and meteorological monitoring. In this paper, we present the development and verification of an airborne Doppler wind lidar (ADWL), featuring an approach proposed to integrate a real-time wind retrieval method with an intelligent processing method for automatic adaptive wind detection. Several verification experiments were conducted to evaluate the measurement effectiveness, including comparisons with a calibrated ground-based Doppler wind lidar (GDWL) and a sounding balloon. Compared with the sounding balloon, the ADWL demonstrated mean errors of 0.53 m/s for horizontal wind velocity and 4.60° for wind direction. The correlation coefficients consistently exceeded 0.98 in all linear analyses. Employed in multiple airborne wind detection events in North China at altitudes up to 6600 m, the ADWL provided effective wind field results with a vertical resolution of 50 m and a data rate of 2 Hz. The wind field results obtained during the detection events validate the ADWL's capabilities in diverse environments and underscore its potential for the comprehensive detection of meteorological information. [ABSTRACT FROM AUTHOR]

Introduction: Inaccurate, untimely diagnoses of fundus diseases leads to vision-threatening complications and even blindness. We built a deep learning platform (DLP) for automatic detection of 30 fundus diseases using ultra-widefield fluorescein angiography (UWFFA) with deep experts aggregation. Methods: This retrospective and cross-sectional database study included a total of 61,609 UWFFA images dating from 2016 to 2021, involving more than 3364 subjects in multiple centers across China. All subjects were divided into 30 different groups. The state-of-the-art convolutional neural network architecture, ConvNeXt, was chosen as the backbone to train and test the receiver operating characteristic curve (ROC) of the proposed system on test data and external test date. We compared the classification performance of the proposed system with that of ophthalmologists, including two retinal specialists. Results: We built a DLP to analyze UWFFA, which can detect up to 30 fundus diseases, with a frequency-weighted average area under the receiver operating characteristic curve (AUC) of 0.940 in the primary test dataset and 0.954 in the external multi-hospital test dataset. The tool shows comparable accuracy with retina specialists in diagnosis and evaluation. Conclusions: This is the first study on a large-scale UWFFA dataset for multi-retina disease classification. We believe that our UWFFA DLP advances the diagnosis by artificial intelligence (AI) in various retinal diseases and would contribute to labor-saving and precision medicine especially in remote areas. [ABSTRACT FROM AUTHOR]

ABSTRACT Analyzing the spatial and temporal variations of offshore crude oil transportation at China's coastal ports is critical for the country's energy security. The automatic identification system provides high‐resolution real‐time data on China's offshore crude oil transportation, offering more precise spatial and temporal insights than existing statistical studies. In this study, we propose a novel method for calculating offshore crude oil transportation volumes by considering the operational status of crude oil tankers as “empty” or “full.” This approach enhances reliability and provides detailed port‐level transport data. Our findings reveal notable similarities between the centrality measures of maritime oil transportation and oil tanker networks, but significant differences in community divisions. Specifically, in the tanker networks, Chinese ports are categorized into three regions, with one region being dominant and the other two regions containing only four ports. In contrast, the oil flow networks show China divided into two major regions and two minor ones. This study offers valuable contributions to strengthening energy security in China and supports informed policy development and implementation. [ABSTRACT FROM AUTHOR]

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Vegetables have been the second largest cultivated crop after grain in China, among which leaf vegetables account for 25% of the total planting area of vegetables. Among them, seedling transplanting is one of the most important steps during planting. Automatic transplanting can be expected as an effective way to fully meet the requirement of single-plant planting of leafy vegetables. However, it is still lacking in the automatic transplanting equipment of leaf vegetables, particularly on the reliability. Two challenges remain in the application of the automatic transplanter in leafy vegetables: first, the sending and dividing seedlings are suitable for long-stem seedlings of solanocarps with developed roots, rather than broad-leaved and short-stem leafy vegetables. Second, the high accuracy of sending and dividing seedlings can lead to the low reliability of the machine and the planting efficiency. Therefore, it is urgent to explore raising seedlings, conveying and separating suitable for the automatic transplanting of leafy vegetables, in order to improve the mechanical planting level of leafy vegetables. In this study, a combined conveying and separation device was designed with the conveyor belt and stop pin for the cabbage substrate block seedlings, particularly for the high success rate and low injury rate of automatic conveying and separating of leafy vegetables. Combined with the mechanical properties of cabbage substrate block seedlings, the theoretical analysis was carried out to determine the stable conveying and separating conditions of the conveying and separating device and the working parameters of the key mechanism. The test bench was built for the single-factor test to determine the range of key factor parameters. The front conveyor belt motor speed, rear conveyor belt motor speed, and frequency of the stop pin were selected as the main test factors. The Box-Behnken central composite test was carried out with the success rate of seedling separation and the substrate block breakage rate as the evaluation indexes. The second-order regression model was established to clarify the influence of various influencing factors on the indexes. The results showed that the better performance of conveying and separating was achieved, when the motor speed of the front conveyor belt was 104 r / min, the motor speed of the rear conveyor belt was 75 r/min, and the frequency of the stop pin was 1.85 s per time. The success rate of seedling separation was 92.73%, and the damage rate of the substrate block was 4.09% in the bench verification test. The success rate of seedling separation was 91.81% and the damage rate of the substrate block was 4.62% in the field verification test. The error values of the two verification tests and the optimization were less than 2 percental points, indicating the high stability. This finding can provide a strong reference for the design of automatic vegetable transplanting equipment. [ABSTRACT FROM AUTHOR]

Widely used agricultural greenhouses are critical in the development of facility agriculture because of not only their huge capacity in food and vegetable supplies, but also their environmental and climatic effects. Therefore, it is important to obtain the spatial distribution of agricultural greenhouses for agricultural production, policy making, and even environmental protection. Remote sensing technologies have been widely used in greenhouse extraction mainly in small or local regions, while large-scale and high-resolution (~ 1-m) greenhouse extraction is still lacking. In this study, agricultural greenhouses in an important agricultural province (Shandong, China) are extracted by the combination of high-resolution remote sensing images from Google Earth and deep learning algorithm with high accuracy (94.04% for mean intersection over union over test set). The results demonstrated that the agricultural greenhouses cover an area of 1755.3 km 2 , accounting for 1.11% of the total province and 2.31% of total cultivated land. The spatial density map of agricultural greenhouses also suggested that the facility agriculture in Shandong has obviously regional aggregation characteristics, which is vulnerable in both environment and economy. The results of this study are useful and meaningful for future agriculture planning and environmental management., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: Mitral regurgitation (MR) is the most common valvular heart disorder, with a morbidity rate of 2.5%. While echocardiography is commonly used in assessing MR, it has many limitations, especially for large-scale MR screening. Cardiac auscultation with electronic stethoscope and artificial intelligence (AI) can be a fast and economical modality for assessing MR severity. Our objectives are (1) to establish a deep neural network (DNN)-based cardiac auscultation method for assessing the severity of MR; and (2) to quantitatively measure the performance of the developed AI-based MR assessment method by virtual clinical trial., Methods and Analysis: In a cross-sectional design, phonocardiogram will be recorded at the mitral valve auscultation area of outpatients. The enrolled patients will be checked by echocardiography to confirm the diagnosis of MR or no MR. Echocardiographic parameters will be used as gold standard to assess the severity of MR, classified into four levels: none, mild, moderate and severe. The study consists of two stages. First, an MR-related cardiac sound database will be created on which a DNN-based MR severity classifier will be trained. The automatic MR severity classifier will be integrated with the Smartho-D2 electronic stethoscope. Second, the performance of the developed smart device will be assessed in an independent clinical validation data set. Sensitivity, specificity, precision, accuracy and F1 score of the developed smart MR assessment device will be evaluated. Agreement on the performance of the smart device between cardiologist users and patient users will be inspected. The interpretability of the developed model will also be studied with statistical comparisons of occlusion map-guided variables among the four severity groups., Ethics and Dissemination: The study protocol was approved by the Medical Ethics Committee of Huzhou Central Hospital, China (registration number: 202302009-01). Informed consent is required from all participants. Dissemination will be through conference presentations and peer-reviewed journals., Trial Registration Number: ChiCTR2300069496., Competing Interests: Competing interests: SC is an employee and owner of Suzhou Melodicare Medical Technology, which developed the Smartho-D2 device., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Neuropsychological test batteries, which accurately and comprehensively assess cognitive functions, are a crucial approach in the early detection of and interventions for cognitive impairments. However, these tests have yet to gain wide clinical application in China owing to their complexity and time-consuming nature. This study aimed to develop the Computerized Neurocognitive Battery for Chinese-Speaking participants (CNBC), an autorun and autoscoring cognitive assessment tool to provide efficient and accurate cognitive evaluations for Chinese-Speaking individuals., Methods: The CNBC was developed through collaboration between clinical neurologists and software engineers. Qualified volunteers were recruited to complete CNBC and traditional neurocognitive batteries. The reliability and validity of the CNBC were evaluated by analyzing the correlations between the measurements obtained from the computerized and the paper-based assessment and those between software-based scoring and manual scoring., Results: The CNBC included 4 subtests and an autorun version. Eighty-six volunteers aged 51-82 years with 7-22 years of education were included. Significant correlations (0.256-0.666) were observed between paired measures associated with attention, executive function, and episodic memory from the CNBC and the traditional paper-based neurocognitive batteries. This suggests a strong construct validity of the CNBC in assessing these cognitive domains. Furthermore, the correlation coefficients between manual scoring and system scoring ranged from 0.904-1.0, indicating excellent inter-rater reliability for the CNBC., Interpretation: A novel CNBC equipped with automated testing and scoring features was developed in this study. The preliminary results confirm its strong reliability and validity, indicating its promising potential for clinical utilization., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Quickly and accurately obtaining information on rice planting areas is crucial for maintaining food security and social stability. Based on the time series of Sentinel-1A images, this study proposes an automatic rice mapping method called the Dynamic Threshold Time-Weighted Dynamic Time Warping method (DT-TWDTW). This method consists of two parts. First, rice samples are extracted automatically by a dynamic threshold method based on the unique flooding signal before and after rice transplanting. Then, the Time-Weighted Dynamic Time Warping (TWDTW) method is used for rice mapping based on the cumulative cost matrix between unknown pixels and rice samples. The results show that the DT-TWDTW method can achieve satisfactory mapping accuracy with an overall accuracy of 94.6%, higher than random forest (89.9%), and support vector machine (91.7%). This study provides a new approach and perspective for remote sensing monitoring of rice, especially for some regions without prior phenological knowledge and collection of the rice samples. [ABSTRACT FROM AUTHOR]

With the continuous development and advancement of modern computer technology, educational information technology has also touched on English composition evaluation. In China, automatic composition evaluation system has also been applied to foreign language writing teaching. Writing is an important comprehensive skill for language learners. The traditional writing feedback is not timely and other problems easily lead to students' lack of writing motivation. At present, there are many advanced automatic composition scoring systems, such as Bingguo intelligent composition scoring. This paper studies the method of applying the automatic composition grading system to college English writing teaching. First, some advantages and disadvantages of the automatic scoring system are illustrated by practical examples in application. Secondly, how to effectively use the automatic scoring system in College English writing teaching is proposed, and then a better teaching model is constructed. From the writing teaching experiment, we can know that the new writing teaching mode urges students to write, maintains the frequency of writing, stimulates students' interest in writing, cultivates students' autonomous writing ability, and improves students' English writing level. [ABSTRACT FROM AUTHOR]

Mountainous regions are prone to dammed lake disasters due to their rough topography, scant vegetation, and high summer rainfall. By measuring water level variation, monitoring systems can detect dammed lake events when mudslides block rivers or boost water level. Therefore, an automatic monitoring alarm method based on a hybrid segmentation algorithm is proposed. The algorithm uses the k-means clustering algorithm to segment the picture scene in the RGB color space and the region growing algorithm on the image green channel to select the river target from the segmented scene. The pixel water level variation is used to trigger an alarm for the dammed lake event after the water level has been retrieved. In the Yarlung Tsangpo River basin of the Tibet Autonomous Region of China, the proposed automatic lake monitoring system was installed. We pick up data from April to November 2021, during which the river experienced low, high, and low water levels. Unlike conventional region growing algorithms, the algorithm does not rely on engineering knowledge to pick seed point parameters. Using our method, the accuracy rate is 89.29% and the miss rate is 11.76%, which is 29.12% higher and 17.65% lower than the traditional region growing algorithm, respectively. The monitoring results indicate that the proposed method is a highly adaptable and accurate unmanned dammed lake monitoring system. [ABSTRACT FROM AUTHOR]

Automatic question generation can help teachers to save the time necessary for constructing examination papers. Several approaches were proposed to automatically generate multiple-choice questions for vocabulary assessment or grammar exercises. However, most of these studies focused on generating questions in English with a certain similarity strategy. This paper presents a mixed similarity strategy which generates Chinese multiple choice distractors with a statistical regression model including orthographic, phonological and semantic features, i.e., features that were shown in previous psycholinguistics studies to contribute to character recognition. In a first experiment, we evaluated the predictive power of the proposed features in measuring Chinese character similarity. One of the significant experimental results showed that the combination of the four proposed categories of features (structure, semantic radical, stroke and meaning) accounts for 62.5 percent of the variance in the human judgments of character similarity. In the second experiment, a user study was conducted to evaluate the quality of system-generated questions using a test item analysis method. Two hundred ninety-six Chinese primary school students (10-11-year-old) participated in this study. We have compared the mixed strategy with another three common distractor generation strategies, orthographic strategy, semantic strategy, and phonological strategy. One of important findings suggested that the mixed strategy significantly outperformed other three strategies in terms of the distractor usefulness and has a highest discrimination power among four strategies.

Background: Ameloblastoma, a common benign tumor found in the jaw bone, necessitates accurate localization and segmentation for effective diagnosis and treatment. However, the traditional manual segmentation method is plagued with inefficiencies and drawbacks. Hence, the implementation of an AI-based automatic segmentation approach is crucial to enhance clinical diagnosis and treatment procedures. Methods: We collected CT images from 79 patients diagnosed with ameloblastoma and employed a deep learning neural network model for training and testing purposes. Specifically, we utilized the Mask R-CNN neural network structure and implemented image preprocessing and enhancement techniques. During the testing phase, cross-validation methods were employed for evaluation, and the experimental results were verified using an external validation set. Finally, we obtained an additional dataset comprising 200 CT images of ameloblastoma from a different dental center to evaluate the model's generalization performance. Results: During extensive testing and evaluation, our model successfully demonstrated the capability to automatically segment ameloblastoma. The DICE index achieved an impressive value of 0.874. Moreover, when the IoU threshold ranged from 0.5 to 0.95, the model's AP was 0.741. For a specific IoU threshold of 0.5, the model achieved an AP of 0.914, and for another IoU threshold of 0.75, the AP was 0.826. Our validation using external data confirms the model's strong generalization performance. Conclusion: In this study, we successfully applied a neural network model based on deep learning that effectively performs automatic segmentation of ameloblastoma. The proposed method offers notable advantages in terms of efficiency, accuracy, and speed, rendering it a promising tool for clinical diagnosis and treatment. [ABSTRACT FROM AUTHOR]

In maritime search and rescue (SAR), commanders need to understand the task execution efficiency of each SAR unit in real time to improve the overall efficiency of SAR efforts. This study proposes a method to evaluate the progress of maritime SAR missions using automatic identification system (AIS) data. First, the positioning accuracy of the AIS data was improved according to the relationship between position, speed, and course. Second, the historical track of the SAR ship was used to generate the SAR completion area based on a line buffer algorithm. The SAR completion area and SAR mission area were then superimposed to determine the overall progress of the SAR mission. The proposed method has been deployed within the SAR software on-board Haixun01 (China's largest and most advanced large-scale cruise rescue ship) since 2017 and has played an important role in devising SAR strategies and tracking mission progress, during several SAR actions. [ABSTRACT FROM AUTHOR]

Background: While automatic speech recognition (ASR) is increasingly used for commercial purposes, its influence on the learners' linguistic performance in terms of oral complexity, accuracy and fluency was under‐explored. To date, few studies have been conducted to investigate how the dictation ASR technology could be incorporated into language classrooms to facilitate the learners' language acquisition. Objectives: This study aimed to examine the effects of ASR‐based technology on English learners' oral accuracy and fluency and depict the corresponding development trajectories. Methods: A total of 160 first‐year university students were enrolled in a 14‐week quasi‐experiment based on a longitudinal research design. Both treatment and control groups were taught with the flipped classroom approach, but the students in the treatment group were particularly required to undertake a pre‐class task with ASR technology. Students' Unit Task performance was audio‐recorded, and the metrics of oral accuracy and fluency were coded and computed based on the recording transcripts. A two‐way repeated measures ANCOVA was conducted to investigate the between‐ and within‐subjects effects as well as the corresponding interaction effects. Results: In terms of the between‐subjects effect, the treatment group outperformed the control group on phonological accuracy, speed fluency and repair fluency. In terms of the within‐subjects effect, significant gains in lexical and morphosyntactic accuracy were witnessed over time in both groups, but the performance of the treatment group tended to be more stable. On all the fluency metrics and phonological accuracy, no significant within‐subjects improvement was seen over time. Implications: The development of oral accuracy may generate a negative impact on that of fluency. Therefore, course developers and teachers need to design special tasks and provide conditions conducive to the development of the students' oral fluency. Moreover, the mobile‐based dictation ASR application could incorporate adaptive artificial intelligence, which may escalate the students' fossilization of learner interlanguage. Lay Description: What is already known about this topic?The ASR‐based technology is becoming popular in foreign language learning.The effect of ASR‐based technology on oral accuracy and fluency is still in a nascent phase. What this paper adds?This study explores how the mobile‐based dictation ASR technology facilitates English learners' oral accuracy and fluency.The dictation ASR‐based application integrated into pre‐class learning enhances English learners' phonological accuracy, speed fluency and repair fluency.The dictation ASR‐based application also contributed to a stable development of English learners' lexical and morphosyntactic accuracy over time. Implications for practiceEnglish teachers could integrate mobile‐based dictation ASR technology into pre‐class practice to improve the students' oral English accuracy and fluency in a flipped learning. [ABSTRACT FROM AUTHOR]

Manual cleaning of feces still remains in many cattle sheds at present. The architectural structure and breeding form cannot fully meet the large-scale production in recent years. Various pathogenic bacteria in feces can also be easy to contaminate the shed and body surface of cattle, leading to lameness and air pollutants. Besides, the independent solid-liquid separation has been a huge hindrance to promoting manure treatment in modern dairy farming. In this study, an automatic fecal-cleaning system was designed with the solid-liquid separation in tethered cattle shed. The new system was installed in the waste ditch, at the end of which a septic tank was built in this case. Specifically, the excrement fell on the conveyer belt, where the liquid flew out through the filter hole on the belt. The electric machine was installed to avoid corrosion at the beginning of the ditch. The solid matter on the belt was moved and fell into the septic tank, as the conveyer belt was driven by the electric machine through the chains and rollers. Belt tensioner and carrier rollers were assembled to prevent skidding and deflection, and further to ensure the smooth running of the system. A trial test was also carried out to obtain the related parameters of the system at an experimental dairy farm in Yangzhou, Jiangsu Province, China in June 2022. A tethered cow shed and all healthy cows were selected as the research objects. The body measurements were performed on the 30 heads of replacement cows and 69 heads of lactating cows. The results indicate that the body length was more outstanding to reflect the individual differences than the body height at the late stage of cows’ growth. The lactating cows shared 75.4% of the total body length in the size of 1.60-1.75 m. The body length showed a significant positive correlation with the feces drop point of cows (P<0.01). If the width of the waste ditch was 40 cm, 79.4% of cows were within the scale excreting completely into the waste ditch. Once the body length of cattle was too long or short, the neck lever lifting or lowering was valid to regulate the excretion position (P<0.01). Thus the efficiency of fecal cleaning was promoted during this time. A simulation experiment of cow’s excretion was conducted to test the moisture content of solid matter, the turbidity of filtrate dilution, and the precipitation volume, in order to compare the filtration effect with different hole diameters. Nevertheless, the filter holes were congested by the feces, as the water fell straight on the solid matter if the hole diameter was shorter than 8 mm. Therefore, the belts were introduced to the different degrees of standing water. The filtration effect of the 8 mm group was not as good as 9, and 10 mm, because of the surface tension of the filtrate. More importantly, the 10 mm group was significantly higher than the 9 mm one, in terms of the precipitation volume in the filtrate. Therefore, 9 mm was proven to be the most suitable diameter for the filter hole. The intensity was calculated as 0.57 MPa far within the tensile strength of the polyvinyl chloride belt, when a hoof stepped on the belt, according to the weight of a cow and the area of a cow’s hoof. Furthermore, the finite element analysis was carried out to calculate the loading density of the system. The maximum deformation was 1.138 cm for the belt with the carrier rollers. The frequency and running speed of the system were also adjusted in practical application, according to the specific situation of cattle’s excretion. The automatic fecal-cleaning system can be expected to realize the solid-liquid separation in cattle sheds. A suitable design of the waste ditch and the cow bed can be greatly conducive to improving the efficiency of cleaning. This finding can provide data support and technical reference to optimize effective waste treatment. [ABSTRACT FROM AUTHOR]

To improve the automation level and operation quality of China's beet harvester and reduce the loss due to damaged and missed excavation, this study used a self-developed sugar beet combine harvester and field simulation experiment platform, based on the single-factor bench test of the automatic row following system in the early stage, taking hydraulic flow A, spring preload B, and forward speed C which have significant influence on performance indices as test factors, and taking the missed excavation rate, breakage rate and reaction time as performance indices, the orthogonal experimental study on the parameter optimization of the three-factor and three-level automatic row following system with the first-order interaction of various factors was carried out. The results of the orthogonal experiments were analyzed using range analysis and variance analysis. The results showed that there were differences in the influence degree, factor priority order and first-order interaction, and the optimal parameter combination on each performance index. A weighted comprehensive scoring method was used to optimize and analyze each index. The optimal parameter combination of the overall operating performance of the automatic row following system was A2B2C1, that is, the hydraulic flow was 25 L/min, the forward speed was 0.8 m/s, and the spring preload was 198 N. Under this combination, the response time was 0.496 s, the missed excavation rate was 2.35%, the breakage rate was 3.65%, and the operation quality was relatively good, which can meet the harvest requirements. The comprehensive optimization results were verified by field experiments with different ridge shapes and different planting patterns. The results showed that the mean values of the missed excavation rate of different planting patterns of conventional straight ridges and extremely large "S" ridges were 2.23% and 2.69%, respectively, and the maximum values were 2.39% and 2.98%, respectively; the average damage rates were 3.38% and 4.14%, and the maximum values were 3.58% and 4.48%, which meet the industry standards of sugar beet harvester operation quality. The overall adaptability of the automatic row following system is good. This study can provide a reference for research on automatic row following harvesting systems of sugar beets and other subsoil crop harvesters. [ABSTRACT FROM AUTHOR]

This study aims to develop a proficient and clinically applicable algorithm that can accurately assess bone age. This algorithm is based on the principles of the Tanner‐Whitehouse 3 (TW3) integral approach, and aims to achieve efficiency, scalability, and interpretability. We developed a model for bone age prediction in children. The model was tested on a pediatric dataset from a tertiary care hospital consisting of left‐hand radiographs of children between the age of 0 and 18. Our model consists of removing the arm portion using a pre‐trained YOLO network, localizing 37 key points in the hand bone portion using a spatial configuration network, and segmenting the original image through 20 of these points to obtain 20 fixed‐size patches. Finally, each of the 20 bone images is classified by training a visual transformer (ViT) model. In this study, a hybrid network, SVTNet, was developed that incorporates visual transformers to obtain estimates of bone age in the carpal (C series) and metacarpal (RUS series) bones. The sum of the clinical TW3 scoring region scores and bone maturity scores were utilized to determine the bone age for each corresponding region. The performance of the algorithm was evaluated in terms of both training and testing by evaluating 3871 left hand X‐ray micrographs obtained from a tertiary hospital in China. The results showed that the average absolute error of bone age estimation was 0.50 years for the RUS series of bones and 0.47 years for the C series of bones. The main contribution of this study is to propose, for the first time, a ViT‐based bone age assessment method that automates the entire process of the TW3 algorithm and is clinically interpretable, with predictive accuracy comparable to that of an experienced orthopedic surgeon. [ABSTRACT FROM AUTHOR]

Objectives We aimed to investigate the value of deep learning (DL) models based on multimodal ultrasonographic (US) images to quantify RA activity. Methods Static greyscale (SGS), dynamic greyscale (DGS), static power Doppler (SPD) and dynamic power Doppler (DPD) US images were collected and evaluated by two expert radiologists according to the EULAR–OMERACT Synovitis Scoring system. Four DL models were developed based on the ResNet-type structure, evaluated on two separate test cohorts, and finally compared with the performance of 12 radiologists with different levels of experience. Results In total, 1244 images were used for the model training, and 152 and 354 for testing (cohort 1 and 2, respectively). The best-performing models for the scores of 0/1/2/3 were the DPD, SGS, DGS and SPD models, respectively (Area Under the receiver operating characteristic Curve [AUC] = 0.87/0.95/0.74/0.95; no significant differences). All the DL models provided results comparable to the experienced radiologists on a per-image basis (intraclass correlation coefficient: 0.239–0.756, P  < 0.05). The SPD model performed better than the SGS one on test cohort 1 (score of 0/2/3: AUC = 0.82/0.67/0.95 vs 0.66/0.66/0.75, respectively) and test cohort 2 (score of 0: AUC = 0.89 vs 0.81). The dynamic DL models performed better than the static ones in most of the scoring processes and were more accurate than the most of senior radiologists, especially the DPD model. Conclusion DL models based on multimodal US images allow a quantitative and objective assessment of RA activity. Dynamic DL models in particular have potential value in assisting radiologists to improve the accuracy of RA US-based grading. [ABSTRACT FROM AUTHOR]

As the core technology of mechanized conservation tillage, no-tillage sowing is a new type of agricultural cultivation method popularized in recent years, it directly sows in the field where the straw and roots of the former crops are left, which not only reduces water evaporation and soil erosion, but also can ensure the germination and growth of seeds, which is conducive to water storage and conservation, increasing production and income, and at the same time improving soil fertility to some extent. However, after the harvest of the former crops without any collection and removal treatment, the “full amount of straw field” has become the new normal state of China's cultivation. The traditional no-tillage seeding equipments are prone to easily increase the workload of straw crushing device when it entering the soil and extinguishing stubble, which causes the blockage and jamming of straw conveying device. In order to solve this problem, the back-throwing type no-tillage straw smashing planter was taken as the research object in this paper, and an automatic control system based on off-ground height of the straw smashing device was developed. Torsional deformation acquisition device was created based on a double fan-shaped through hole metal detection disc and proximity switch, which was installed at both ends of the driving shaft to effectively monitor the rotation speed of the driving shaft and the stagger angle caused by the change of torque load. The design was based on the 32-bit ARM CortexTM-M3 core microprocessor hardware platform, which constituted the data acquisition and output drive unit of the automatic control system, the driving shaft rotation speed and stagger angle was collected in real time, the change trend was analysed through the system software, and current working conditions was identified. According to the corresponding output control signal, the straw height adjusting mechanism timely corrects the off-ground height of the straw smashing and conveying device to stabilize the working load. The straw height adjustment mechanism was composed of a solenoid valve, a hydraulic cylinder, a fixed bracket and a supporting ground wheel. The hydraulic cylinder's high pressure driving oil was taken directly from the working tractor to improve the applicability of the device. The performance verification test showed that under the driving shaft’s rated rotation speed of 2 500 r/min, when the height of the straw crusher off the ground changed the working load, the automatic control system controled the rotation speed of the driving shaft between 2448 and 2632 r/min, the change of the stagger angle of the driving shaft was ± 0.002 4 rad, and the response delay time of the control signal to the change of the stagger angle was 0.24 s. The field operation comparison test showed that when the back-throwing type no-tillage straw smashing planter was equipped with automatic control system based on off-ground height of the straw smashing device, the passability of the machine was greatly improved, the occurrence of blockage was eliminated, the operation efficiency was increased by 52.9%, and the height of surface stubble was reduced by 43.4% after straw smashing operation. In conclusion, The design of this paper uses the change trend of the driving shaft rotation speed and stagger angle to identify the working condition, effectively eliminating the influence of mechanical structure parameters and material differences, and the mechanical structure is light and easy to install, which can provide reference for qualitative monitoring of torque load of agricultural machinery. [ABSTRACT FROM AUTHOR]

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Introduction: To compare the morphological classification ability of peripheral‐blood leukocytes of the automatic cell morphology analyzers MC‐100i and DI‐60. Methods: (1) MC‐100i and DI‐60 were used to analyze leukocytes in 432 venous blood samples collected from three tertiary hospitals across China. The preclassification results were compared with the results reported by senior morphological experts (postclassification results) to evaluate the accuracy, sensitivity, specificity, and consistency of leukocyte preclassification for both instruments. (2) In 200 of the 432 blood samples, morphological experts conducted manual microscopic examination for various types of leukocytes. The correlation between the MC‐100i and DI‐60 leukocyte postclassification results and the expert microscopist results were analyzed. Results: (1) MC‐100i preclassified leukocytes and nucleated red blood cells (RBCs). Compared with the postclassification results, the total leukocyte preclassification accuracy of MC‐100i was 97.16%, while that of DI‐60 was 87.24%. The sensitivity of MC‐100i to abnormal cells (including blasts, promyelocytes, neutrophilic myelocytes, neutrophilic metamyelocytes, reactive lymphocytes, abnormal promyelocytes, plasma cells, abnormal lymphocytes and nucleated RBCs) was 90.24%, which was significantly higher than the 50.72% sensitivity of DI‐60. (2) Comparing the postclassification results with manual microscopy, except for reactive lymphocytes and basophils, the MC‐100i and DI‐60 results had good correlations with various leukocyte types and nucleated RBCs (r > 0.85), and MC‐100i was better than DI‐60 in the recognition of basophils. Conclusion: Both MC‐100i and DI‐60 have good detection ability for five normal types of leukocytes in peripheral blood. MC‐100i has significantly better detection sensitivity for abnormal cells in peripheral blood than DI‐60. [ABSTRACT FROM AUTHOR]

The topographic map plays a very important role in economic construction. In the process of drawing topographic maps, different symbols represent different ground objects, but the symbols representing complex ground objects are often complicated and difficult to create. Moreover, the creation process of complex map symbols can seriously affect the efficiency of topographic map production. Therefore, this paper proposes an automatic derivation method for creation of complex map symbols in a topographic map. The data used are new geographic entity data under the background of Chinese new fundamental surveying and mapping situation. Firstly, four derivation modes of complex map symbols are summarized, including feature-point mode, centroid mode, feature-line mode, and parallel-line mode; then, using the four modes singly or in combination, the complex map symbols of the topographic map are directly derived from the geographic entity data based on programming, and the topographic map cartographic result is obtained automatically. Finally, some topographic maps for Shanxi Province, China, is used for the validation of the creation of map symbols. The experimental results show that the proposed method can automatically derive the complex map symbols of the topographic map, greatly improving production efficiency and obtaining a good visualization effect. The proposed method is a new approach for a new situation and realizes the transformation and upgrading of fundamental surveying and mapping achievements. [ABSTRACT FROM AUTHOR]

To reasonably ensure good groundwater quality for the safety of users, it is necessary to monitor the evolution of physicochemical parameters of water. This study assessed the evolution of groundwater quality in Xianyang City (Central China) for 6 years and forecasted the water quality index (WQI) using the automatic exponential smoothing model (AESM). The results revealed that pH, Mn, NH4+, HCO3− and Ca2+ increased significantly within 6 years. However, there was a slight decrease in K+, NO3−, SiO2, and Mg2+. Over the course of 6 years, seven out of fourteen wells maintained excellent or good water quality, whereas groundwater quality in the remaining wells fluctuated from excellent to good or from good to poor. The WQI index increased for nine wells, and five of them including well 173, well 457, well 46, well 26, and well C4 manifested an exponential increase of WQI, which was mainly caused by high concentrations of Mn, NH4+, and NO3−. The forecast accuracy verified by MASE and sMAPE, which ranged from 0.35 to 1.06 and from 0.04 to 0.69, respectively, demonstrated that the predictions were reliable. The AESM was tested in groundwater quality prediction, indicating that it might be used for actual scientific studies. [ABSTRACT FROM AUTHOR]