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Background: Peer assessment has played an important role in large‐scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop an effective automated grading model to analyse the data and predict learners' learning results. Objectives: The present study aimed to propose an automated grading model with reliability detection. Methods: A total of 109,327 instances of peer assessment from a large‐scale teacher online learning program were tested in the experiments. The reliability detection approach included three steps: recurrent convolutional neural networks (RCNN) was used to detect grade consistency, bidirectional encoder representations from transformers (BERT) was used to detect text originality, and long short‐term memory (LSTM) was used to detect grade‐text consistency. Furthermore, the automated grading was designed with the BERT‐RCNN model. Results and Conclusions: The effectiveness of the automated grading model with reliability detection was shown. For reliability detection, RCNN performed best in detecting grade consistency with an accuracy rate of 0.889, BERT performed best in detecting text originality with an improvement of 4.47% compared to the benchmark model, and LSTM performed best with an accuracy rate of 0.883. Moreover, the automated grading model with reliability detection achieved good performance, with an accuracy rate of 0.89. Compared to the absence of reliability detection, it increased by 12.1%. Implications: The results strongly suggest that the automated grading model with reliability detection for large‐scale peer assessment is effective, with the following implications: (1) The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results. (2) This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. (3) This solution could shift educational institutions from labour‐intensive grading procedures to a more efficient educational assessment pattern, allowing for more investment in supporting instructors and learners to improve the quality of peer feedback. Lay Description: What is already known about this topic: Peer assessment has played an important role in large‐scale online learning, as it helps promote the effectiveness of learners' online learning.Issues such as disagreement between peer assessors, rough assessment, and plagiarism in large‐scale online learning can decrease peer assessment reliabilityIncorporating extensive data into a training model may result in grading uncertainties. What this paper adds: Detecting the peer assessment reliability before grading is essential in the context of large‐scale online learning.This study aimed to propose and validate an automated grading model with reliability detection for the large‐scale online peer assessment, which will help improve the effectiveness of automated grading, combining the advantages of computer technology and human expertise. Implications for practice and/or policy: The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results.This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. [ABSTRACT FROM AUTHOR]

In response to the dangerous behavior of pedestrians roaming freely on unsupervised train tracks, the real-time detection of pedestrians is urgently required to ensure the safety of trains and people. Aiming to improve the low accuracy of railway pedestrian detection, the high missed-detection rate of target pedestrians, and the poor retention of non-redundant boxes, YOLOv5 is adopted as the baseline to improve the effectiveness of pedestrian detection. First of all, L1 regularization is deployed before the BN layer, and the layers with smaller influence factors are removed through sparse training to achieve the effect of model pruning. In the next moment, the context extraction module is applied to the feature extraction network, and the input features are fully extracted using receptive fields of different sizes. In addition, both the context attention module CxAM and the content attention module CnAM are added to the FPN part to correct the target position deviation in the process of feature extraction so that the accuracy of detection can be improved. What is more, DIoU_NMS is employed to replace NMS as the prediction frame screening algorithm to improve the problem of detection target loss in the case of high target coincidence. Experimental results show that compared with YOLOv5, the AP of our YOLOv5-AC model for pedestrians is 95.14%, the recall is 94.22%, and the counting frame rate is 63.1 FPS. Among them, AP and recall increased by 3.78% and 3.92%, respectively, while the detection speed increased by 57.8%. The experimental results verify that our YOLOv5-AC is an effective and accurate method for pedestrian detection in railways. [ABSTRACT FROM AUTHOR]

This paper examines how the design and implementation of a core teacher education course develops pre-service teachers’ information communication technology (ICT) in education competencies in a mainland Chinese university. This course adopted a four-component instructional design system to develop its curriculum, incorporated an inquiry-based learning approach to develop pre-service teachers’ pedagogical competencies, and used an online learning platform to facilitate the sharing of experiences of using ICT for teaching and learning. This case study demonstrates that innovative pedagogies together with more relevant course content supported by an online learning platform may enhance the effectiveness of a core ICT in education course. [ABSTRACT FROM PUBLISHER]

Highlights: [•] The wear resistance was improved 100× over unfilled PTFE when adding 1wt.% Ti3SiC2. [•] The 10wt.% sample reached the lowest wear rate of 2.1×10−6 mm3/Nm. [•] Ti3SiC2/PTFE composites show better wear resistance than graphite/PTFE composites. [•] Ti3SiC2 can speed up the formation of the transfer film and the oxide film. [ABSTRACT FROM AUTHOR]

With the advancement of technology, various types of downhole tools have been developed. It is necessary to ensure that downhole tools can be tripped to the predetermined well depth through a drilling and completion string without any damage. However, mathematical models to predict the downhole tool deformation under axial loading are lacking. In this paper, an eccentric tube deflection model is established according to the geometric characteristics of downhole tools. First, six contact cases of an eccentric tube under axial loading in a curved wellbore are considered. The model solves the eccentric tube deflection in the different contact cases through the beam-column model and energy method. Second, using this model, the critical axial load in the contact cases is examined along with the deflection, bending moment, and contact force of the eccentric tube. The results show that the deflection, bending moment, and contact force of the eccentric tube are related to the contact case. The bending moment and contact force of the eccentric tube in wrap contact under axial compression are higher than those in other contact cases. In particular, when the eccentric tube is subjected to wrap contact under axial compression, the maximum bending moment and contact force increase sharply, and this aspect can help evaluate the accessibility of the downhole tool. Finally, this paper proposes a new method based on the eccentric tube deflection model to analyze the accessibility of a completion string in the context of eccentric downhole tools. The effectiveness of the proposed method is demonstrated through a field case. • A deflection model of eccentric tube in curved wellbores is established. • Critical axial load, bending moment and contact force are obtained. • The influence of offset on deflection, bending moment and contact force of eccentric tube cannot be neglected. • A method for evaluating the accessibility of a completion string with eccentric downhole tools is proposed via a field case. [ABSTRACT FROM AUTHOR]

Corrosion fatigue is an important factor that limits the life of grid materials including wire clips. In order to study the effect of corrosion fatigue and to select suitable grid steels, this paper focuses on the corrosion fatigue properties of Q235 carbon steel, Q235 galvanized steel, and 316L stainless steel in the corrosive environments of air, 2wt% NaCl, 5wt% NaCl, and 8wt% NaCl. Through the fatigue test in the corrosive environment, and the surface morphology scanning and microstructure observation of the fracture, the following conclusions are drawn: the three materials are more susceptible to corrosion fatigue in the Cl− environment, and the higher the Cl− concentration, the greater the likelihood of fracture caused by corrosion fatigue for these three materials. By analyzing the surface roughness, dimples, and cracks in the microstructure, it is found that 316L stainless steel is highly sensitive to Cl− corrosion under cyclic stress, and Q235 galvanized steel is more resistant to Cl−. By plotting the stress fatigue life curve of Q235 galvanized steel, it is found that the corrosion fatigue life decreases as the Cl− concentration increases. For wire clips in areas with severe Cl− pollution, Q235 galvanized steel should be selected to achieve the best anti-corrosion fatigue effect; at the same time, the original parts should be repaired or replaced in a timely manner based on the predicted corrosion fatigue life. [ABSTRACT FROM AUTHOR]

Emotions play an important role in the learning process. With intelligent technology support, identification and intervention of learners' cognition have made great achievement, but the care of emotion has been in the absence for a long time. In recent years, the use of affective computing technology to solve affective loss in online education has become a key research topic. To date, a growing number of studies have investigated automated emotion recognition (AER) in online environments. However, AER has been mainly studied from the perspective of computer science focusing on technical characteristics of developing AI technology while its pedagogical value and educational application has been overlooked. Therefore, this systematic literature review aimed to bring together educational and technical aspects of AER. Following PRISMA methodology, a comprehensive search of AER research from 2010 to 2024 in three databases (Web of Science, Science Direct and IEEE Xplore) identified 117 studies that met inclusion criteria. The articles were coded for report characteristics, educational characteristics (tech platform, pedagogy, assessment, content), technical characteristics (emotion model, emotion category, emotion measurement channel, database, algorithm model) and outcome characteristics (technical result, educational application). We found that the primary purpose of these studies was to develop and evaluate systems for AER, rather than implementing these systems in real online learning environments. Furthermore, our findings indicated a lack of integration between computer science and educational science in the realm of AER. Despite the fact that most algorithm models demonstrated high accuracy in AER, the interpretability of the results was significantly constrained by the quality of the databases used, along with the scarcity of studies focusing on the effective and real-time application of AER results. These findings provide essential guidance for shaping future research and development pathways in this field. • AER studies emphasize system development not practical online learning application. • Educational aspects are often neglected in AER research. • Self-built databases with multimodal data support AER integration into education. • AER's real-world utility depends on interpretable results, not just high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, the atmospheric environmental corrosion grade of substations in 11 prefecture level cities in Shanxi Province is evaluated through a metal hanging sheet experiment. Combined with the main environmental factors, such as temperature, relative humidity, and the concentration of main pollution factors, such as SO2, Cl−, dust, etc., the influence of various factors on the corrosion of transmission and transformation equipment is analyzed, and the corresponding anti-corrosion measures are put forward. [ABSTRACT FROM AUTHOR]

[Display omitted] • Effects of different-type diluents on manipulating solvation chemistry are highlighted. • An evolutive solvation structure and interfacial model are also presented. • Diverse behaviors for Li depositions and corresponding interfacial chemsitry are compared. • The full cells based high-voltage cathode with different systems are also explored. The growth of dendrites on lithium metal anode is troublesome because it induces irreversible capacity loss and safety hazards. Formulating localized high-concentration electrolytes (LHCEs) is an alternative solution to satisfy these challenges. However, the role of diluents in tailoring interfacial chemistry remains to be explicated. Herein, we decipher the direct relationship between different-type diluents and Li-coordinated solvation at the molecular scale. Compare to hydrofluoroethers (HFE), the "active" difluoro ethylene carbonate (DFEC) delivers a pronounced interaction with Li+ and 1, 2-dimethoxyethane (DME) molecule, resulting in a compressed but more accumulated aggregates (AGGs) solvation shell, assisting in the expected chemical nature of SEI with defluorination of DFEC. Consequently, DFEC-based LHCEs (D-LHCEs) realize a record-long lifespan over 3000 h with dendrite-free at 1 mA cm−2 and outstanding dendrite tolerance even under 3 mA cm−2 in symmetric cells. The assembled full cells with D-LHCEs outperform a superior capacity retention of ∼86.83 % with average CE of ∼99.42 % after 500 cycles at 0.5C and the remarkable cycling performance even under harsh conditions (including pouch cells or with controlled N/P of 1.8) in 4.45 V-level Li||LiCoO 2 cells. This work highlights the decisive role of diluents in pursuing high-efficiency electrolytes for high-energy Li metal batteries. [ABSTRACT FROM AUTHOR]

Background. Endometrial cancer (EC) is a common tumor of the genital tract that affects the female reproductive system but with only limited treatment options. We aimed to discover new prognostic biomarkers for EC. Methods. We used mRNA-seq data to detect differentially expressed genes (DEGs) between EC and control tissues. Detailed clinicopathological information was collected, and changes in the mRNA and protein levels of hub DEGs were analyzed in EC. Copy number variation (CNV) was also evaluated for its association with the pathogenesis of EC. Gene set enrichment analysis (GSEA) was conducted to enrich significant pathways driven by the hub genes. Cox regression analysis was used to select variables to create a nomogram. The nomogram was calibrated by applying the concordance index (C-index), and net benefits of the nomogram at different threshold probabilities were quantified using decision curve analysis (DCA). Results. Differential expression analysis identified 24 DEGs as potential risk factors for EC. Survival analysis revealed that TPX2 expression was related to worsening overall survival in patients with advanced EC. A high CNV was associated with the overexpression of TPX2; this suggested that modifications in the cell-cycle pathway might be crucial in the advancement of EC. Moreover, an individualized nomogram was developed for TPX2 incorporating clinical factors; this was also evaluated for its ability to predict EC. Calibration and DCA analyses confirmed the robustness and clinical usefulness of the nomogram. Conclusion. We offer novel insights into the pathogenesis and molecular mechanisms of EC. The overexpression of TPX2 was related to a poorer prognosis and could serve as a biomarker for predicting prognostic outcomes in EC patients. [ABSTRACT FROM AUTHOR]

The intelligent auxiliary decision-making (IADM) is emerging as a feasible solution for air traffic control (ATC) to reduce undesirable conflicts in shared airspace; meanwhile, unmanned aerial vehicles (UAVs) can be operated with enhanced efficiency and safety using IADM. This paper presents the conflict risk framework of the MAV\UAV flight that improves flight safety of MAVs and UAVs in shared airspace. This is accomplished by focusing on two steps: First, determine the minimum safety communication interval between the UAV and controller; second, build a conflict risk model to detect which decision mechanism will minimize risk. Our approach provides a standard model to start with to improve IADM and allow engineers to focus on the operational purpose of MAV/UAV. Results show that our work presented here is practical and straightforward, and it brings an evident engineering application prospect. [ABSTRACT FROM AUTHOR]

Abstract In recent years, machine learning has been widely applied to problems in detecting network attacks, particularly novel attacks. However, traditional machine learning methods depend heavily on feature engineering, and extracting features is often time-consuming and complex. Thus, it is impractical to detect attacks with traditional machine learning methods in real-time applications. To discover network attacks efficiently, we propose an end-to-end detection approach. We implement deep learning models to analyze payloads and propose a convolutional neural network-based payload classification approach (PL-CNN) and a recurrent neural network-based payload classification approach (PL-RNN) for use in attack detection. Our two approaches learn feature representations from original payloads without feature engineering and support end-to-end detection. These approaches achieve accuracies of 99.36% and 99.98% when applied to the DARPA1998 dataset, respectively; these accuracies are comparable to or better than those of state-of-the-art methods. In addition, our methods are efficient and practical. [ABSTRACT FROM AUTHOR]

In a 2013-2014 Australia-China Council project, middle school students in Australia and China shared digital stories about their everyday lives and local cultures, and traditional tales with a modern twist. This article reports on research that aimed to explore the successes and challenges associated with this digital story exchange between Australia and China as a pedagogical approach to support language learning and intercultural understanding. An interpretivist approach was taken, focusing on the perspectives of the teachers. According to the teachers, the exchange was successful to a degree in supporting students' learning in the areas of language, intercultural understanding and twenty-first-century skills, including digital literacies and technological skills, and helped teachers extend their pedagogical horizons. A number of challenges also arose. Analysis of interview data revealed that both the successes and challenges fell into four interrelated domains, which we have labelled structures, practices, capabilities and technologies. This article offers new insights into the exchange of multimodal digital stories as learning activities in the Australian-Chinese context and provides recommendations to guide educators in these four domains. [ABSTRACT FROM AUTHOR]