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The effect of technology on primary students’ self-regulated vocabulary learning (SRVL) over time and its dynamic relationship with vocabulary outcomes have been scarcely studied. This quasi-experimental study reports a longitudinal inquiry into the effect of a mobile-assisted self-regulation scheme on primary students’ SRVL and the relationship between the changes in the perceived SRVL skills and vocabulary learning outcome. The study lasted seven months. Participants were 174 Grade 4 students (89 girls) from four classes at a primary school in Mainland China. Two classes were randomly assigned to the experimental and control groups. Students in both groups used the app named Vocab+, the former with and the latter without a mobile-assisted self-regulation scheme. Data collection included questionnaires on students’ perceived SRVL skills and vocabulary tests. Latent Growth Modelling (LGM) was used to analyse data. The results showed that perceived SRVL skills and vocabulary learning outcomes increased over time. The findings further revealed that students in the experimental group exhibited a steeper increasing trend in perceived SRVL skills and vocabulary learning outcomes. Besides, the association between the growth rates of students’ perception of SRVL skills and vocabulary learning outcomes was stronger with the experimental group than with the control group. Our results provided theoretical implications for understanding the relationship between SRVL skills and vocabulary learning outcomes from a developmental perspective.

Existing studies have evidenced that mobile-assisted vocabulary learning, which allows students to create artefacts and engage in productive skills, can help them in vocabulary learning. However, there remains little research and validated approaches for designing seamless vocabulary learning experiences for young learners. In this paper, a seamless vocabulary learning system was specifically designed for young learners. The study involved three classes of primary 2 students to investigate the learning effectiveness of seamless vocabulary learning, with a focus on analyzing student-generated artefacts. The results validated the effectiveness of seamless vocabulary learning for young learners, emphasizing the importance of strengthening the connection between home-based learning and classroom-based learning. The study also indicated the pivotal role of providing students with opportunities to improve their artefacts constantly across different learning contexts.

An isogeometric topology optimization (ITO) model for multi-material structures under thermal-mechanical loadings using neural networks is proposed. In the proposed model, a non-uniform rational B-spline (NURBS) function is employed for geometric description and analytical calculation, which realizes the unification of the geometry and computational models. Neural networks replace the optimization algorithms of traditional topology optimization to update the relative densities of multi-material structures. The weights and biases of neural networks are taken as design variables and updated by automatic differentiation without derivation of the sensitivity formula. In addition, the grid elements can be refined directly by increasing the number of refinement nodes, resulting in high-resolution optimal topology without extra computational costs. To obtain comprehensive performance from ITO for multi-material structures, a weighting coefficient is introduced to regulate the proportion between thermal compliance and compliance in the loss function. Some numerical examples are given and the validity is verified by performance analysis. The optimal topological structures obtained based on the proposed model exhibit both excellent heat dissipation and stiffness performance under thermal-mechanical loadings.

The rapid evolution of generative artificial intelligence (AI) and virtual reality (VR) technologies are revolutionising various fields, including education and gaming industries. However, studies on how to enhance immersive game-based learning with AI and VR technologies remain scant. Given this, the article presents the creation of “LearningverseVR,” an immersive game-based learning platform developed using generative AI and VR technologies, which is based on “Learningverse,” a metaverse platform developed by the lead author and her research team. The “LearningverseVR” platform uses Unity as the client and Python, Flask and MySQL as the backend. Unity's multiplayer service provides multiplayer online functionality, supporting learners to engage in immersive and interactive learning activities. The design framework of the platform consists of two main components: Game-based learning with generative AI and immersion with VR technologies. First, generative AI is used to create NPCs with diverse personalities and life backgrounds, and enable learners to interact with NPCs without scripted dialogues, creating an interactive and immersive game-based learning environment. Secondly, such a learning experience is enhanced by leveraging the Large Language Model (LLM) ecosystem with VR technology. The creation of the “LearningverseVR” platform provides novel perspectives on digital game-based learning.

Near-infrared laser radiation based on Nd3+-doped yttrium ortho-aluminate (Nd:YAlO3, Nd:YAP) has garnered significant interest regarding solid-state lasers. Nevertheless, the crystal microstructures and electronic characteristics of Nd:YAP are still unclear, and the unique physical properties underlying its enormous applications require clarification. In this study, we conducted first-principles calculations at the atomic level to explore the electronic properties and mechanical characteristics of both pure YAP and Nd3+-doped YAP. The results suggest that the substitution of the Y3+ ion site with the Nd3+ impurity ion induces slight structural distortion in the YAP crystal lattice. An impurity band emerges between the original conduction band and the valence band, attributed to the 4f orbital of the Nd3+ ion, exerting a substantial influence on the narrowing of the band gap. Through an analysis of the mechanical characteristics of both pure YAP and Nd:YAP, we conclude that the incorporation of Nd3+ atoms leads to a reduction in the mechanical properties of YAP to a certain extent. Our study can serve as a foundational data source for investigations into material performance, especially for the application of Nd:YAP in solid-state laser systems.

The role of self-regulated learning in language learning has been widely acknowledged, and there is a growing number of studies on technology-enhanced self-regulated language learning (SRLL). This systematic review aims to provide a holistic picture of existing studies in this area by identifying the characteristics of published studies, the research methods used to evaluate SRLL effectiveness and the role of technology in SRLL. The review covered 34 empirical studies focusing on SRLL that were published from 2011 to 2020. The results showed varied characteristics of technology-enhanced SRLL studies, dominance of the use of quantitative methods, greater focus on examining students’ SRLL outcomes instead of their processes, and the role of technology in supporting the performance phase of students’ SRLL instead of the entire SRLL process. These findings have implications for using technologies to facilitate and examine the holistic process of students’ SRLL.

Complex hierarchical structures and diverse personnel mobility pose challenges for many multi-level organizations. The difficulty of reasonable human resource planning in multi-level organizations is mainly caused by ignoring the hierarchical structure. To address the above problems, firstly, a multi-level organization human resource network optimization model is constructed by representing the turnover situation of multi-level organizations in a dimensional manner as a multi-level network. Secondly, we propose an improved late acceptance hill climbing based on tabu and retrieval strategy (TR-LAHC) and designed two intelligent optimization operators. Finally, the TR-LAHC algorithm is compared with other classical algorithms to prove that the algorithm provides the best solution and can effectively solve the personnel mobility planning problem in multi-level organizations.

Earth observation satellites provide services for users by taking images. The rapid increase in the number of satellites and missions makes mission planning more difficult. In order to solve this problem, this paper adopts a multi-population evolutionary algorithm. First, a mixed-integer programming model of the joint observation satellites mission planning problem (JOSMPP) is constructed. After that, a dual-population artificial bee colony algorithm (DPABC) and a heuristic task scheduling algorithm are proposed. Two learning-based nectar generation methods are used to guide the direction of population optimization. The search population is used to lead the nectar search at the stage of employed bees and supplement population is used to improve search performance. According to the performance of these two populations, candidate populations are generated to realize the search of onlooker bees. After each generation of population search, the composition of the two populations is adjusted according to the performance. Finally, the experimental results show that the DPABC algorithm has more advantages than multiple state-of-the-art algorithms to solve the joint mission planning problem of earth observation satellites.

In line with the substantial interest in STEM (science, technology, engineering, and mathematics) education and the major projects in STEM curriculum development around the world, efforts should be particularly made to increase the supply of STEM teachers through proper and effective teacher professional development. Although there have been a number of studies related to teacher professional development for individual subject training in science, technology, engineering and mathematics, quality research on professional development for teachers to develop their capacity for adopting the integrative and cross-disciplinary approaches advocated in STEM education remains in its infancy. The theme of this special issue is two-fold: (a) to provide researchers and practitioners in STEM education with a scholarly platform for reflecting on what challenges and impediments STEM teachers have encountered, and (b) to exchange new theoretical and practical insights gained from empirical research on designing, enacting and evaluating professional development programmes for building teachers’ capacity in STEM education.

Abstract Background Mounting evidence has suggested the essential role of long non-coding RNAs (lncRNAs) in a plethora of malignant tumors, including hepatocellular carcinoma. However, the underlyling mechanisms of lncRNAs remain unidentified in HCC. The present work was aimed to explore the regulatory functions and mechanisms of LncRNA LNCAROD in HCC progression and chemotherapeutic response. Methods The expression of LNCAROD in HCC tissues and cell lines were detected by quantitative reverse transcription PCR (qPCR). Cancer cell proliferation, migration, invasion, and chemoresistance were evaluated by cell counting kit 8 (CCK8), colony formation, transwell, and chemosensitivity assays. Methylated RNA immunoprecipitation qRCR (MeRIP-qPCR) was used to determine N6-methyladenosine (m6A) modification level. RNA immunoprecipitation (RIP) and RNA pull down were applied to identify the molecular sponge role of LNCAROD for modulation of miR-145-5p via the competing endogenous RNA (ceRNA) mechanism, as well as the interaction between LNCAROD and serine-and arginine-rich splicing factor 3 (SRSF3). The interaction between insulin-like growth factor 2 mRNA-binding protein 1 (IGF2BP1) and LNCAROD was also identified by RIP assay. Gain- or-loss-of-function assays were used to identify the function and underlying mechanisms of LNCAROD in HCC. Results We found that LNCAROD was significantly upregulated and predicted a poorer prognosis in HCC patients. LNCAROD upregulation was maintained by increased m6A methylation-mediated RNA stability. LNCAROD significantly promoted HCC cell proliferation, migration, invasion, and chemoresistance both in vitro and in vivo. Furthermore, mechanistic studies revealed that pyruvate kinase isoform M2 (PKM2)-mediated glycolysis enhancement is critical for the role of LNACROD in HCC. According to bioinformatics prediction and our experimental data, LNCAROD directly binds to SRSF3 to induce PKM switching towards PKM2 and maintains PKM2 levels in HCC by acting as a ceRNA against miR-145-5p. The oncogenic effects of LNCAROD in HCC were more prominent under hypoxia than normoxia due to the upregulation of hypoxia-triggered hypoxia-inducible factor 1α. Conclusions In summary, our present study suggests that LNCAROD induces PKM2 upregulation via simultaneously enhancing SRSF3-mediated PKM switching to PKM2 and sponging miR-145-5p to increase PKM2 level, eventually increasing cancer cell aerobic glycolysis to participate in tumor malignancy and chemoresistance, especially under hypoxic microenvironment. This study provides a promising diagnostic marker and therapeutic target for HCC patients.

BackgroundAlthough the roles of m6A modification in the immune responses to human diseases have been increasingly revealed, their roles in immune microenvironment regulation in coronary heart disease (CHD) are poorly understood.MethodsThe GSE20680 and GSE20681 datasets related to CHD were acquired from the Gene Expression Omnibus (GEO) database. A total of 30 m6A regulators were used to perform LASSO regression to identify the significant genes involved in CHD. Unsupervised clustering analysis was conducted using the m6A regulators to distinguish the m6A RNA methylation patterns in patients with CHD. The differentially expressed genes (DEGs) and biological characteristics, including GO and KEGG enrichment results, were assessed for the different m6A patterns to analyse the impacts of m6A regulators on CHD. Hub genes were identified, and subsequent microRNAs-mRNAs (miRNAs–mRNAs) and mRNAs-transcriptional factors (mRNA-TFs) interaction networks were constructed by the protein and protein interaction (PPI) network method using Cytoscape software. The infiltrating proportion of immune cells was assessed by ssGSEA and the CIBERSORT algorithm. Quantitative real-time PCR (qRT-PCR) was performed to detect the expression of the significant m6A regulators and hub genes.ResultsFour of 30 m6A regulators (HNRNPC, YTHDC2, YTHDF3, and ZC3H13) were identified to be significant in the development of CHD. Two m6A RNA methylation clusters were distinguished by unsupervised clustering analysis based on the expression of the 30 m6A regulators. A total of 491 genes were identified as DEGs between the two clusters. A PPI network including 308 mRNAs corresponding to proteins was constructed, and 30 genes were identified as hub genes that were enriched in the bioprocesses of peptide cross-linking, keratinocyte differentiation. Twenty-seven hub genes were found to be related to miRNAs, and seven hub genes were found to be related to TFs. Moreover, among the 30 hub genes, eight genes were found to be upregulated in CHD, and three were found to be downregulated in CHD compared to the normal people. The high m6A modification pattern was associated with a higher infiltrated abundance of immune cells.ConclusionOur findings demonstrated that m6A modification plays crucial roles in the diversity and complexity of the immune microenvironment in CHD.

TGA is one of the members of TGACG sequence-specific binding protein family, which plays a crucial role in the regulated course of hormone synthesis as a stress-responsive transcription factor (TF). Little is known, however, about its implication in response to bacterial wilt disease in potato (Solanum tuberosum) caused by Ralstonia solanacearum. Here, we performed an in silico identification and analysis of the members of the TGA family based on the whole genome data of potato. In total, 42 StTGAs were predicted to be distributed on four chromosomes in potato genome. Phylogenetic analysis showed that the proteins of StTGAs could be divided into six sub-families. We found that many of these genes have more than one exon according to the conserved motif and gene structure analysis. The heat map inferred that StTGAs are generally expressed in different tissues which are at different stages of development. Genomic collinear analysis showed that there are homologous relationships among potato, tomato, pepper, Arabidopsis, and tobacco TGA genes. Cis-element in silico analysis predicted that there may be many cis-acting elements related to abiotic and biotic stress upstream of StTGA promoter including plant hormone response elements. A representative member StTGA39 was selected to investigate the potential function of the StTGA genes for further analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) assays indicated that the expression of the StTGAs was significantly induced by R. solanacearum infection and upregulated by exogenous salicylic acid (SA), abscisic acid (ABA), gibberellin 3 (GA3), and methyl jasmonate (MeJA). The results of yeast one-hybrid (Y1H) assay showed that StTGA39 regulates S. tuberosum BRI1-associated receptor kinase 1 (StBAK1) expression. Thus, our study provides a theoretical basis for further research of the molecular mechanism of the StTGA gene of potato tolerance to bacterial wilt.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s13046-021-02090-7.

Learning analytics (LA) has been increasingly using in teaching and learning. However, in the field of applied linguistics, the use of LA has only begun to touch the surface. There is a need for understanding how LA and learning design (LD) influence each other and provide useful information to language teachers in the context of specific courses or learning environments. In this study, a retrospective analysis was conducted to identify the factors influencing LA for collaborative language learning in classrooms, from a holistic perspective by integrating LA, LD, and teacher inquiry. The findings suggested that (1) LA focused on interactions can inform pedagogical refinement effectively when LD in language learning is premised on social constructivist theories; (2) LA supported teacher inquiry and LD on condition that the teacher holds innovation-oriented beliefs and the participatory culture between teachers and researchers. The study provided insights into the use of LA in collaborative language learning classrooms that focus on collecting and evaluating learners’ interaction process beyond gleaning linguistic or behavioral facts. Professional development implications and future research are also addressed.

The “Curse of Dimensionality” induced by the rapid development of information science might have a negative impact when dealing with big datasets, and it also makes the problems of symmetry and asymmetry increasingly prominent. Feature selection (FS) can eliminate irrelevant information in big data and improve accuracy. As a recently proposed algorithm, the Sparrow Search Algorithm (SSA) shows its advantages in the FS tasks because of its superior performance. However, SSA is more subject to the population’s poor diversity and falls into a local optimum. Regarding this issue, we propose a variant of the SSA called the Tent Lévy Flying Sparrow Search Algorithm (TFSSA) to select the best subset of features in the wrapper-based method for classification purposes. After the performance results are evaluated on the CEC2020 test suite, TFSSA is used to select the best feature combination to maximize classification accuracy and simultaneously minimize the number of selected features. To evaluate the proposed TFSSA, we have conducted experiments on twenty-one datasets from the UCI repository to compare with nine algorithms in the literature. Nine metrics are used to evaluate and compare these algorithms’ performance properly. Furthermore, the method is also used on the coronavirus disease (COVID-19) dataset, and its classification accuracy and the average number of feature selections are 93.47% and 2.1, respectively, reaching the best. The experimental results and comparison in all datasets demonstrate the effectiveness of our new algorithm, TFSSA, compared with other wrapper-based algorithms.

The connected vehicle environment is significant for the future road network. For constructing the connected vehicle environment, real-time data acquirement is always the prerequisite. Recently, using Light Detection and Ranging (LiDAR)-based roadside infrastructures are becoming a prevalent method of obtaining real-time traffic data. However, the collected raw data from LiDAR cannot usually be used directly. The steps of data processing, like background filtering and object detection, are necessary. The processed data can then be employed in different applications. This paper proposed a novel layer-based searching method that is established with the help of the point distribution features to distinguish moving objects from the point cloud. It aimed to address the unexpected influence of factors such as congested situations and package loss. The new approach was also evaluated compared with the state-of-the-art methods by applying field data. The results showed that the proposed method is more effective than other methods. This method may be applicable to other types of rotating LiDAR for improving the background filtering performance.

Recently, many chaos-based image encryption algorithms have been proposed. Most of them adopt the traditional confusion-diffusion framework. Multiple encryption rounds or one round of complex encryption is typically executed in these schemes to realize high security. However, these operations will reduce the computational efficiency. To overcome these issues, we propose a novel key-substitution encryption architecture (KSA). Under the proposed KSA, we design a key scheming for updating the initial keys using the plain-image and develop a new substitution method for encrypting various types of images. The simulation results and security analysis demonstrate the superior security and high efficiency of the proposed image encryption algorithm using the KSA (KSA-IEA), which executes only one round of encryption.

Abstract As the number of satellite emergency imaging tasks grows, the main goal of satellites becomes putting forward solutions and meeting users’ demands in a relatively short time. This study aims to investigate the problem of emergency task planning for agile satellites. Through the analysis of the problem and its constraints, a model of emergency task autonomous planning was implemented. According to the characteristics of emergency tasks, a strategy to deal with the tasks of different emergency levels and various quantities was proposed. We put forward three algorithms for quick insertion of emergency tasks, i.e., emergency task insertion algorithm (ETIA), general emergency task insertion algorithm (GETIA), and general emergency task planning &insertion algorithm (GETPIA). The experimental result showed that the strategy and algorithms can not only respond quickly to observation tasks but also produce effective planning programs to ensure the successful completion of observation tasks.

The purpose of this study is to improve the heat economy of generator-unit boiler feed pump (BFP) drive and promote the driving effect of BFP. To this end, this paper studies the heat economy of generator’s BFP drive. A large-scale coal-fired thermal power generating unit was taken as an example. Then, with reference to the current situation of each link in the feed pump drive chain, modern driving technology was applied, and the mathematical model of the drive scheme was established using the fixed flow analysis method to perform the calculation for specific case. The study found that for the BFP of the 600MW unit, the net powers are sorted by size in the following: traditional electric pump scheme < steam pump scheme < high-efficiency fluid coupling scheme < frequency conversion scheme < spindle scheme. Therefore, among the five drive schemes, the spindle drive scheme has the least links in the energy transfer process, so its heat economy is the best.

This study compared the impacts of different weed managements on weed community, soil health and economic performance between the wheat–maize (WM) and garlic–soybean (GS) rotations. A total of four treatments (H0T, tillage without herbicide; H0T0, without both herbicide and tillage; HT, both herbicide and tillage; HT0, herbicide without tillage) were designed for both rotations. A total of 16 weed species were recorded in the WM rotation, with life forms of 62% for annuals, 12% for annual + perennial and 20% for perennials. While in the GS rotation, there were 17 weed species, with 71% being annuals. When crop rotation changed from WM to GS, the topsoil layer seed bank (0–5 cm) decreased by 137%. GS rotation always had higher earthworm densities than that of WM under the same condition. Organic weed control (H0T, H0T0) from both WM and GS added more soil organic matters than the chemical methods (HT and HT0). Economically, up to 69% higher net profit had been achieved in the GS than WM for their organic products. This study provides an ecological basis to guide organic farming practices, especially for weed management in the future.

The production of aerospace shell products is directly related to the safety and reliability of aerospace products. In this work, the aerospace shell product digital production line scheduling problem (ASPDPLSP) was studied, and a solution was developed. In the production process, it is necessary to decide the processing machine and time of each operation. In order to create a scientific shell product production plan, we propose an operation scheduling algorithm (OSA). Based on the constraints of the inspection process, the OSA has two heuristic task scheduling rules. Then, in order to further optimize the product production plan, an improved genetic algorithm (IGA) is proposed. Considering the repeatability caused by random search, a method for the initial population generation with similar and diverse characteristics is proposed. Two of these generation rules retain symmetry and randomness. IGA was used to optimize the order in which the products were processed, resulting in lower costs. Simulation experiments showed that the proposed algorithm solved ASPDPLSP well and provided suggestions to produce aerospace shell products.