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AimThis paper aims to investigate the relationship between social support and rural teachers’ work engagement while exploring the mediating effect of mindfulness in teaching and the moderating effect of psychological safety.MethodsA sample of 866 rural teachers was recruited, in order to complete the Mindfulness in Teaching Scale, Social Support Rating Scale, Psychological Safety Scale, and Utrecht Work Engagement Scale.ResultsThe study findings indicate that: (1) social support positively influenced the work engagement of rural teachers; (2) further, mindfulness in teaching partially mediated the effect of social support on rural teachers’ work engagement; (3) psychological safety moderated the second half of the pathway of “social support → mindfulness in teaching → work engagement” while the positive correlation between mindfulness in teaching and work engagement was stronger among rural teachers with high psychological safety.ConclusionSocial support documented a strong correlation with work engagement while mindfulness in teaching mediated the pathway between the aforementioned variables. Furthermore, psychological safety moderated the second half of the mediated pathway (the link from mindfulness in teaching to work engagement). Hence, the study outcomes reveal the influential mechanism of social support on the work engagement of rural teachers. This finding suggests that we need to further improve the social support system and its effect mechanism in order to improve the rural teachers’ work engagement. At the same time, it is also very important to create a good psychological working environment to ensure that they maintain a good level of mindfulness in teaching.

Scientific development provides opportunities to replace many traditional manual methods to achieve more accurate results and higher efficiency. To scientifically ascertain the geographical origin of coffee, this study develops a method for the rapid determination of 16 inorganic elements in coffee using microwave digestion combined with the ICP-MS internal standard method. Principal component analysis (PCA), Fisher discriminant analysis (FDA), and Partial least squares discriminant analysis (PLS-DA) are employed to analyze 40 coffee samples from three production areas of Pu'er, Baoshan, and Wanning. The results show that the linear correlation coefficients of the 16 elements in this method are above 0.999, the detection limits are in the range of 0.0004–0.63 mg/kg, the RSD of the precision experiments are 4.5 %–13.5 %, the recovery rate of the peak experiment is 86.0 %–96.3 % with the RSD of 1.1 %–8.8 %, and the results of the standard substances are within the range of standard values. Using the discriminant analysis of inorganic elements in coffee (FDA and PLS-DA), coffee origin discrimination was realized, and six key elements (Al, Mn, Fe, Cu, Na, and Ba) are identified as effective discriminatory indexes. Accordingly, a coffee origin discrimination model is established, and the overall accuracy discrimination rate of the discrimination model are all more than 90.0 %, and FDA > PLS-DA. The findings indicated that the method has good accuracy and reliability, is suitable for analyzing and determining multiple elements in sample components as targets, and may have a positive impact on the development of related industries.

The effects of direct aging treatment (at 300 °C for 5 hours) on selective laser melted (SLMed) Al-4.5Mn-1.5Mg-0.9Sc-0.2Zr alloy were investigated in this work, with the microstructure, fatigue behaviors, and fracture characteristics examined to determine the primary cause of fatigue crack source. The results revealed that the microstructure of the investigated alloy comprised fine equiaxed and columnar grains. Upon aging treatment, a significant number of nano-scaled Al3(Sc, Zr) precipitates were dispersed within the grains, leading to a substantial increase in strengths. The yield strength improved from 431 MPa to 568 MPa, representing an increase of more than 32%, while the fatigue strength improved from 180 MPa to 220 MPa after aging treatment. Nevertheless, the fracture toughness decreased significantly from \begin{document}$ 25.1\; {\rm{MPa}}\cdot {\sqrt{\rm m}} $\end{document} to \begin{document}$ 12.3 \;{\rm{MPa}}\cdot \sqrt{\rm m} $\end{document}. The results of the fatigue fracture characteristics indicate that the Mn-rich phase and the formation of defects such as pores and poor powder fusion are the sources of fatigue cracking. Although direct aging treatment can significantly increase the yield strength, decrease the rate of fatigue crack propagation, and thus improve the fatigue performance, it deteriorates the fracture toughness, and thus shortens the fatigue life of the alloy as well.

Abstract Influenza virus infection causes increased morbidity and mortality in the elderly. Aging impairs the immune response to influenza, both intrinsically and because of altered interactions with endothelial and pulmonary epithelial cells. To characterize these changes, we performed single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA sequencing (bulk RNA-seq) on lung tissue from young and aged female mice at days 0, 3, and 9 post-influenza infection. Our analyses identified dozens of key genes differentially expressed in kinetic, age-dependent, and cell type-specific manners. Aged immune cells exhibited altered inflammatory, memory, and chemotactic profiles. Aged endothelial cells demonstrated characteristics of reduced vascular wound healing and a prothrombotic state. Spatial transcriptomics identified novel profibrotic and antifibrotic markers expressed by epithelial and non-epithelial cells, highlighting the complex networks that promote fibrosis in aged lungs. Bulk RNA-seq generated a timeline of global transcriptional activity, showing increased expression of genes involved in inflammation and coagulation in aged lungs. Our work provides an atlas of high-throughput sequencing methodologies that can be used to investigate age-related changes in the response to influenza virus, identify novel cell-cell interactions for further study, and ultimately uncover potential therapeutic targets to improve health outcomes in the elderly following influenza infection.

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.

With the advancement of science and artificial intelligence, education is experiencing significant innovation. The adaptive learning system is emerging as a promising approach to achieving personalized learning. The cognitive model plays a crucial role as the fundamental rationale behind the adaptive learning system. Currently, there is no uniform and highly operational method for constructing cognitive models. This study adopts Interpretive Structural Modeling (ISM) as the foundational approach for constructing a cognitive model of solid geometry. Based on literature and expert opinions, 17 cognitive attributes of high school solid geometry were identified. Subsequently, a questionnaire survey involving 40 experts was conducted to establish the contextual relationships among these attributes. Applying the ISM method resulted in a seven-level model. This model was then revised based on expert opinions to create the final cognitive model, revealing three primary paths within the domain of high school solid geometry.This paper contends that the use of the ISM method for constructing cognitive models is effective and objective. The resulting cognitive model unveils the content structure of high school solid geometry, and provides an innovative perspective on the construction of cognitive models.

The level of difficulty of mathematical test items is a critical aspect for evaluating test quality and educational outcomes. Accurately predicting item difficulty during test creation is thus significantly important for producing effective test papers. This study used more than ten years of content and score data from China’s Henan Provincial College Entrance Examination in Mathematics as an evaluation criterion for test difficulty, and all data were obtained from the Henan Provincial Department of Education. Based on the framework established by the National Center for Education Statistics (NCES) for test item assessment methodology, this paper proposes a new framework containing eight features considering the uniqueness of mathematics. Next, this paper proposes an XGBoost-based SHAP model for analyzing the difficulty of mathematics tests. By coupling the XGBoost method with the SHAP method, the model not only evaluates the difficulty of mathematics tests but also analyzes the contribution of specific features to item difficulty, thereby increasing transparency and mitigating the “black box” nature of machine learning models. The model has a high prediction accuracy of 0.99 for the training set and 0.806 for the test set. With the model, we found that parameter-level features and reasoning-level features are significant factors influencing the difficulty of subjective items in the exam. In addition, we divided senior secondary mathematics knowledge into nine units based on Chinese curriculum standards and found significant differences in the distribution of the eight features across these different knowledge units, which can help teachers place different emphasis on different units during the teaching process. In summary, our proposed approach significantly improves the accuracy of item difficulty prediction, which is crucial for intelligent educational applications such as knowledge tracking, automatic test item generation, and intelligent paper generation. These results provide tools that are better aligned with and responsive to students’ learning needs, thus effectively informing educational practice.

The Learning Cycle Model (LCM) is an inquiry-based teaching strategy and curriculum model that has been researched and practiced for almost half a century and has achieved good educational results in science education, especially in primary and secondary schools. This study collected LCM educational research data to better understand the recent progress of LCM educational research. It used the bibliometric analysis software CiteSpace for the first time to sort out the overall development of LCM. Compared to other literature review methods, we obtained a more comprehensive picture of the current research hotspots and future research trends in LCM research. First, we searched 498 articles from the Web of Science core collection between 2000 and May 16, 2023. The trajectory of LCM research was identified by analyzing publication trends, authors, countries/regions, and research institutions. Secondly, we obtained the keyword co-occurrence map and clustering map through CiteSpace's built-in algorithm to analyze the main areas and research frontiers of this study. Meanwhile, the development trend is shown by co-keyword burst detection. After analysis, this study found that the current research hotspot findings focus on conceptual learning, validity research, and integration with different teaching processes. The research frontiers are mainly related to teacher professional development and research on the impact of LCM on learning outcomes. Finally, this study discusses future research directions, including research on the impact of LCM on 21st century skills, comparison with other modes of inquiry teaching and learning, and its use in engineering education, to promote better development of LCM.

Abstract Regulatory T (Treg) cells are inevitable to prevent deleterious immune responses to self and commensal microorganisms. Treg function requires continuous expression of the transcription factor (TF) FOXP3 and is divided into two major subsets: resting (rTregs) and activated (aTregs). Continuous T cell receptor (TCR) signaling plays a vital role in the differentiation of aTregs from their resting state, and in their immune homeostasis. The process by which Tregs differentiate, adapt tissue specificity, and maintain stable phenotypic expression at the transcriptional level is still inconclusivei. In this work, the role of BATF is investigated, which is induced in response to TCR stimulation in naïve T cells and during aTreg differentiation. Mice lacking BATF in Tregs developed multiorgan autoimmune pathology. As a transcriptional regulator, BATF is required for Treg differentiation, homeostasis, and stabilization of FOXP3 expression in different lymphoid and non‐lymphoid tissues. Epigenetically, BATF showed direct regulation of Treg‐specific genes involved in differentiation, maturation, and tissue accumulation. Most importantly, FOXP3 expression and Treg stability require continuous BATF expression in Tregs, as it regulates demethylation and accessibility of the CNS2 region of the Foxp3 locus. Considering its role in Treg stability, BATF should be considered an important therapeutic target in autoimmune disease.

Using education survey data from 153, 317 Grade 4 students and 150, 040 Grade 8 students in China, this study examined the relationship between time on homework and academic achievement and learning anxiety with hierarchical linear modeling (HLM) and classification and regression tree (CART) approaches. With a classification of time spent on homework into four related variables, this study found that, firstly, time spent on in-school homework during weekdays had positive effects on students’ achievement for both grades, and the positive effect was stronger for Grade 8 students than Grade 4 students. Moreover, a maximum of 1 h was recommended for Grade 4 students. Secondly, time spent on out-of-school homework on weekdays was negatively correlated with students’ academic achievement and positively with learning anxieties. It had greater detrimental effect on Grade 8 than Grade 4. Thirdly, Grade 8 students were encouraged to have more out-of-school homework on weekend with more than 2.8 h on average recommended. It was expected to complement extant studies and provide the practical findings for teachers, practitioners and school policy makers in making any homework assignment planning or conducting interventions.

Abstract The presence of pesticide residues in cowpea raises serious health concerns. In this study, a novel, sensitive, high-performance method was developed to simultaneously analyze the residues of 35 pesticides in cowpea samples from growing areas in the Hainan province of China, from November 2018 to June 2021. The method employs modified QuEChERS sample pretreatment coupled with gas chromatography-tandem mass spectrometry. The limits of quantification of the 35 pesticides in the cowpea matrix ranged from 1.0 to 8.0 μg/kg. Twenty-seven of the 35 pesticides were detected, twelve of which are banned for use on legumes in China. Residues for ten pesticides in 17.1% of the samples exceeded their MRLs, with the highest exceedance of 380% observed in difenoconazole. Moreover, 80.8% of the samples contained one or more pesticide residues, with the most frequently detected pesticide being chlorfenapyr with a detection rate of 46.3%. In addition, the pesticide triazophos was detected through different years and regions. Notably, the chronic dietary exposure risk (%ADI) of the detected pesticides, evaluated from the national estimated acceptable daily intake, was lower than 100% in Chinese people of different age groups.

In this study, lightweight thermal insulation materials were successfully prepared in this study using a high-temperature micro-foaming method at 1200°C, where granite waste was the main raw material and SiC was the foaming agent. Further, the thermal insulation material’s macrostructure, microstructure, room temperature, and high-temperature performance have been investigated. This material has a small closed-cell porosity of up to 73.8%, high compressive strength of 18.2 MPa, low thermal conductivity of 0.22 W/(m•K) at room temperature and 0.17 W/(m•K) at 350°C, positive reheating linear change, and the refractoriness under load of 812°C, confirming that these materials can be used as good thermal insulation materials up to ~800°C.

To better understand the latest developments in global science, technology, engineering, and mathematics (STEM) education research, this study collected STEM education research materials to sort out the development of STEM education as a whole, so as to get a clearer path and trend of STEM education development. This study conducted a visualization and quantitative analysis of the literature on STEM education research in Science Citation Index Extended (SCI-E) and Social Science Citation Index (SSCI) using the CiteSpace (5.8.R3) tool. First, the basic information of STEM education was analyzed in terms of annual publication volume, authors, countries, and research institutions. Secondly, the main fields, basic contents and research hotspots of this research were analyzed by keyword co-occurrence and keyword time zone mapping. Finally, the research frontiers and development trends are presented through co-citation clustering and high-frequency keyword bursts. The research hotspots are focused on engineering education, teachers’ professional development, and gender differences. The research frontiers are mainly related to teacher professional development, 21st century skills, early childhood creativity, and gender differences. This study systematically analyzes the latest developments in global STEM education research, which is beneficial for readers to understand the full picture of STEM education research so that researchers can conduct more in-depth studies and promote better development of STEM education. The number of analyzed literature is limited. We only analyzed articles from SSCI and SCI-E databases, and the articles were written in English. In addition, we only analyzed the literature and lacked empirical studies on the findings of the literature.

Ability of data analysis, as one of the essential core qualities of modern citizens, has received widespread attention from the international education community. How to evaluate students’ data analysis ability and obtain the detailed diagnosis information is one of the key issues for schools to improve education quality. With an employment of cognitive diagnostic assessment (CDA) as the basic theoretical framework, this study constructed the cognitive model of data analysis ability for 503 Grade 9 students in China. The follow-up analyses including the learning path, learning progression and corresponding personalized assessment were also provided. The result indicated that first, almost all the students had the data awareness. Furthermore, the probability of mastering the attribute Interpretation and inference of data was relatively low with only 60% or so. Also, the probabilities of mastering the rest of attributes were about 70% on average. It was expected that this study would provide a new cognitive diagnostic perspective on the assessment of students’ essential data analysis abilities.

Value-added assessments have become a reasonable and accepted assessment method for education and teaching. Mathematics reading ability is an important ability in mathematics learning which provides a prerequisite for solving mathematical problems. With the aim of uncovering the effects of mathematics reading ability on the continuous development of mathematics learning, this study focuses on the value added to students’ mathematics reading ability as well as their mathematics performance. From a longitudinal perspective, we collected academic achievement data for 463 s-grade students, including their scores on their mathematics reading ability, which were then used a developed measurement tool. Building on Weiss’s “Theory of Change”, the students were divided into four categories: high academic achievement and high value-added, low academic achievement and high value-added, low academic achievement and low value-added, and high academic achievement and low value-added. Finally, we discussed the impact of the students’ reading abilities in mathematics on their overall achievement. This study reveals a close correlation between mathematics reading skills and value-added performance. Higher scores in mathematics reading indicate higher value-added levels. For students with initially high scores, their mathematics reading skills greatly contributed to their high value-added performance.

Summary: Na/K-ATPase (NKA), besides its ion transporter function, is a signal transducer by regulating Src family kinases (SFK). The signaling NKA contributes to oxidized LDL-induced macrophage foam cell formation and interacts with TLR4. However, its role in lipopolysaccharides (LPS)-induced signaling and glycolytic switch in macrophages remains unclear. Using peritoneal macrophages from NKA α1 haploinsufficient mice (NKA α1+/−), we found that NKA α1 haploinsufficiency led to enhanced LPS-stimulated NF-κB pathway, ROS signaling, and pro-inflammatory cytokines. Intraperitoneal injection of LPS resulted in more severe lung inflammation and injury with lower survival rate in NKA α1+/− mice. Additionally, LPS induced a higher extent of the metabolic switch from oxidative phosphorylation to glycolysis. Mechanistically, NKA α1 interacted with TLR4 and Lyn. The presence of NKA α1 in this complex attenuated Lyn activation by LPS, which subsequently restricted the downstream ROS and NF-κB signaling. In conclusion, we demonstrated that NKA α1 suppresses LPS-induced macrophage pro-inflammatory signaling through Lyn.

Discourse analysis, as a mainstream research method in classroom teaching, has gained widespread attention in education. Educators believe that children's thinking development requires support from interactive discourse. In this study, four primary school mathematics classes were segmented based on the form, frequency, content, and purpose of teacher-student interactions. A total of 73 dialogue segments were selected for coding, resulting in 338 codes. The coding process was based on the turn of talk and assigned corresponding coding numbers to the content of teacher-student discourse in the fragments according to the Bloom-Turney teaching questioning code list and the Hierarchical Framework of Student Thinking Level based on Biggs-Collis Structure of the observed learning outcome. The results show that Knowledge level question (Q1), Understanding level question (Q2), Application level question (Q3), Synthesis level question (Q5), and Evaluation level question (Q6) are related to students' low-level thinking. The questions of Analysis level (Q4), Synthesis level (Q5), and Evaluation level (Q6) are related to students' high-level thinking. We found that there are variety of interactive structures between teachers and students in the question and answer session, among which three interaction structures show significant performance, namely Q2 [right arrow] M (Multiple-point structural level) [right arrow] Q4 [right arrow] C (Correlational structural level), Q3 [right arrow] M [right arrow] Q4 [right arrow] C, Q3 [right arrow] M [right arrow] Q6 [right arrow] A (Abstract-extension level), these structures can show how teachers timely adjust the types of questions according to students' answers to improve students' thinking level.

The emergence of artificial intelligence has made adaptive learning possible, but building an adaptive system requires a comprehensive understanding of students’ cognition. The cognitive model provides a crucial theoretical framework to explore students’ cognitive attributes, making it vital for learning assessment and adaptive learning. This study investigates 52 experts, including primary and secondary school teachers, mathematics education experts, and graduate students, based on the 16 cognitive attributes in the TIMSS 2015 assessment framework. Through an analysis of their attribute questionnaires, the Interpretive structural modeling (ISM) method is used to construct a five-level mathematical cognitive model. The model is then revised through oral reports and expert interviews, resulting in a final cognitive model ranging from “memorize” to “justify”. The cognitive model describes the relationship between different attributes in detail, enabling the development of adaptive systems and aiding in the diagnosis of students’ cognitive development and learning paths in mathematics.

Abstract The synergistic effects of surface treatment and interleaf on the interlaminar mechanical properties of glass fiber-aluminum laminates were studied. Aluminum sheets were treated with alkaline etching. Meanwhile, a graphene oxide (GO) interleaf was introduced between the aluminum sheet and the glass fiber-reinforced epoxy composite. Double cantilever beam and end-notched flexure tests were employed to evaluate the interlaminar fracture toughness of the glass fiber-aluminum laminates. The obtained results show that the toughening efficiency of the interleaf is dependent on the aluminum surface characteristics as well as the GO loading. Further comparison reveals that the highest mode-I and mode-II fracture toughnesses are obtained in the specimens with alkali etching treatment and addition of GO interleaf with 0.5 wt% of GO loading, which are 510% and 381% higher in comparison to the plain specimen. Fracture surfaces were observed to further uncover the reinforcement mechanisms.

Low-salinity tolerance and growth are crucial economic traits of shrimp in areas with low-salinity water. In the current study, an F1 full-sib family of Pacific white shrimp (Litopenaeus vannamei) was obtained by the “semidirectional cross” method, and 2 parents and 146 progenies were genotyped using double-digest restriction site-associated DNA sequencing technology (ddRAD-Seq). A total of 3335 high-quality markers were identified and used to construct a genetic linkage map. This genetic map comprised 48 linkage groups (LGs), and the total map length spanned 3954.462 centimorgan (cM) with an average marker interval of 1.186 cM. Three quantitative trait loci (QTLs) for low-salinity tolerance and 3 QTLs for growth (body weight) were detected under the condition of restricted logarithmic odds (LOD) score ≥ 2.50. With the assistance of the genome of L. vannamei and relevant annotations, 3 potential growth-related genes (sob, TJP1 and ZBTB24) and 3 low-salinity response candidate genes (Rnf168, CNOT2 and RPB3) were identified from the genomic scaffolds. Moreover, the qPCR results showed that the expression levels of the 3 candidate genes were significantly affected by low-salinity stress, indicating that they possibly play an important role in low-salinity stress. Here, we present the first report of mapping of low-salinity tolerance QTLs in the genetic mapping of L. vannamei, which will be beneficial for the selection of shrimp breeding markers and might help promote the study of the L. vannamei genome.

Scientific explanation is one of the most core concepts in science education, and its mastery level is crucial for a deep understanding of the nature of science. As a new generation of assessment theory, cognitive diagnostic assessment (CDA) can get the knowledge of students' mastery of fine-grained knowledge. Based on the extant research, this research has formed eight attributes of scientific explanation concepts. By coding the Trends in International Mathematics and Science Study (TIMSS) test items, a CAD tool was formed. Data collected from 574 Grade 4 students in Hangzhou, China, combined with the data of the United States, Singapore, Australia, the United Kingdom, and Russia, were used in our study. The Deterministic Inputs, Noisy “And” gate (DINA) model was used to analyze the results from three aspects: the probability of mastery of attributes, the international comparison of knowledge states, and the analysis of learning paths. This study provided a new perspective from a CDA approach on the assessment of scientific explanation.

C-Jun NH2-terminal kinase (JNK)-interacting protein 4 (JIP4) is an essential molecule that organizes and links components of the mitogen-activated protein kinase (MAPK) pathway. In the present study, a novel JIP4 gene was identified in Litopenaeus vannamei and designated Lv-JIP4. The full-length Lv-JIP4 cDNA was 3923 bp in length and contained a 170-bp 5′-untranslated region (UTR), a 72-bp 3′-UTR, and a 3,681-bp open reading frame (ORF). The Lv-JIP4 protein contained three functional domains, including the Jnk-SapK_ap_N, JIP_LZII and WD40 domains. Lv-JIP4 mRNA was broadly distributed in all studied tissues, with the highest level in muscle. The in situ hybridization results showed that the Lv-JIP4 was scattered in muscle fibres and near the sarcolemma. Subcellular localization analysis indicated that Lv-JIP4 was located in the cytoplasm but not in the nucleus. The transcript levels of Lv-JIP4 in muscle were found to be significantly increased by low temperature, low pH, high salinity, poly(I:C), and LPS challenges, indicating that it might play a crucial role in responding to adverse stresses. The transcript levels of Lv-JIP4, Lv-p38aMAPK and Lv-p38bMAPK in muscle were significantly upregulated after challenge with low temperature, suggesting that Lv-JIP4 may be the positive regulator of Lv-p38aMAPK and Lv-p38bMAPK under low temperature stress conditions. In addition, after knockdown of Lv-JIP4 expression, the protein levels of total Lv-p38MAPK and phospho-Lv-p38MAPK in the experimental dsRNA group were attenuated compared with those in the control group after 48 h (h), 60 h and 72 h of low-temperature stress, indicating that it might activate and phosphorylate the Lv-p38MAPK protein in response to cold stress. Here, we elucidated the molecular characteristics and the potential role of Lv-JIP4 and further refined the signal transduction mechanism of L. vannamei in response to low-temperature stress.

Wild chrysanthemum has a high medicinal value. Its mechanized harvest can improve harvesting efficiency, reduce labor costs and improve planting benefits, which is an important way to promote artificial planting. However, one of the difficulties in mechanized harvesting is the large diameter and hardness of the stem, leading to high cutting resistance and power consumption. In order to reduce cutting resistance and power consumption, a bionic cutting blade is designed in this paper by employing the bionics principle and the contour of the cricket’s upper jaw incisor lobe instead of the sharp triangular teeth of the standard harvester blade. Using the finite element method, the cutting-edge angle, cutting angle, and reciprocating speed were taken as test factors. The maximum shear force and power consumption were taken as evaluation indexes. At the same time, the center combination simulation test was carried out to optimize the cutting body and to determine the optimal cutting speed. When the cutting-edge angle was 21°, the cutting angle was 66°, the reciprocating speed was 1.29 m/s, and the maximum shear force and power consumption were minimal. The results showed that the maximum shear force of the bionic cutter was reduced by 18% and the power consumption by 15.8%. The bench test showed that the maximum shear force and power consumption of the bionic cutter were reduced by 10.5% and 10.8%, respectively, when the entire wild chrysanthemum stem was cut. The results can provide a reference for the mechanical harvesting of wild chrysanthemum stems.

A crushed-rock embankment (CRE) with a high porous crushed-rock layer (CRL) can effectively cool the underlying permafrost through natural ventilation within the layer. However, in addition to the ambient conditions, the ventilation efficiency of the CRL and its cooling effect are significantly affected by the pavement width. In this study, the local wind flow around an embankment section was first analyzed based on field monitoring data. Then, considering climate warming, a 2-D coupled model of heat and mass transfer was established to investigate the wind characteristics and the cooling effects of the CRE with different pavement widths. The results showed that the pavement width exerted considerable impacts on the wind characteristics and cooling effects of the CRE. These impacts were evaluated via variations in the wind speed, the permafrost table, and the soil temperatures. An increase in pavement width can lower the wind speed within the CRL, which is adverse to the long-term thermal regimes of the embankment and the underlying permafrost. In addition, due to differential wind flows around the embankments, an asymmetric distribution of the soil temperatures beneath the windward and leeward sides of the embankments existed. Overall, it is hoped that the results of this study can provide informative references for the Qinghai–Tibet expressway that is constructed in permafrost regions.

Due to a lack of an accurate model in finite element simulation of mechanized harvesting of wild chrysanthemum, the stem of wild chrysanthemum in the harvesting period is taken as the research object. ANSYS Workbench 19.0 software and LS-DYNA software (LS-PrePOST-4.3-X64) are used to calibrate the finite element simulation model of wild chrysanthemum stem cutting. The stem diameter distribution at the cutting height of the chrysanthemum is obtained. The maximum shear forces at different diameters (7 mm, 8 mm, 9 mm, 10 mm, and 11 mm) within the cutting range are determined as 120.0 N, 159.2 N, 213.8 N, 300.0 N, and 378.2 N, respectively, by using a biomechanical testing machine and a custom-made shear blade. The Plastic_Kinematic failure model is used to simulate the cutting process by the finite element method. The Plackett–Burman test is employed to screen out the test factors that significantly affect the results, namely, the yield stress, failure strain, and strain rate parameter C. The regression model between the shear force and significant parameters is obtained by central composite design experiments. To obtain the model parameters, the measured values are substituted into the regression equation as the simulation target values. In other words, the yield stress is 17.96 MPa, the strain rate parameter C is 87.27, and the failure strain is 0.0387. The maximum shear force simulation test is carried out with the determined parameters. The results showed that the maximum error between the simulated and the actual value of the maximum shear force of wild chrysanthemum stems with different diameters is 7.8%. This indicates that the calibrated parameters of the relevant stem failure model can be used in the finite element method simulation and provide a basis for subsequent simulations.

As one of the most influential international large-scale educational assessments, the Program for International Student Assessment (PISA) provides a valuable platform for the horizontal comparisons and references of international education. The cognitive diagnostic model, a newly generated evaluation theory, can integrate measurement goals into the cognitive process model through cognitive analysis, which provides a better understanding of the mastery of students of fine-grained knowledge points. On the basis of the mathematical measurement framework of PISA 2012, 11 attributes have been formed from three dimensions in this study. Twelve test items with item responses from 24,512 students from 10 countries participated in answering were selected, and the analyses were divided into several steps. First, the relationships between the 11 attributes and the 12 test items were classified to form a Q matrix. Second, the cognitive model of the PISA mathematics test was established. The liner logistic model (LLM) with better model fit was selected as the parameter evaluation model through model comparisons. By analyzing the knowledge states of these countries and the prerequisite relations among the attributes, this study explored the different learning trajectories of students in the content field. The result showed that students from Australia, Canada, the United Kingdom, and Russia shared similar main learning trajectories, while Finland and Japan were consistent with their main learning trajectories. The primary learning trajectories of the United States and China were the same. Furthermore, the learning trajectory for Singapore was the most complicated, as it showed a diverse learning process, whereas the trajectory in the United States and Saudi Arabia was relatively simple. This study concluded the differences of the mastery of students of the 11 cognitive attributes from the three dimensions of content, process, and context across the 10 countries, which provided a reference for further understanding of the PISA test results in other countries and shed some evidence for a deeper understanding of the strengths and weaknesses of mathematics education in various countries.

Three low-grade mineral raw materials, i.e., natural dolomite, high-silicon magnesite, and forsterite tailings were used to prepare a MgO–2CaO·SiO _2 –3CaO·SiO _2 composite refractory, and its hydration resistance was essentially improved. Free CaO was converted into more stable calcium silicate which protected MgO from hydration, and low melting phases formed by impurities promoted densification. The composite refractory, predominantly composed of MgO, β -2CaO·SiO _2 , and 3CaO·SiO _2 with a bulk density of 3.13 g cm ^−3 and apparent porosity of 4.6%, was obtained after being calcined at 1600 °C for 3 h. The prepared composite exhibited excellent hydration resistance with a weight increase of only 0.03% at 1600 °C. Microstructural analysis revealed that a dense matrix consisting of intergranular calcium–silicate phases, was dispersed by rounded MgO aggregates.

NUCB2¹⁻⁸³ has been recently reported as an anorexigenic and anti-hyperglycemic peptide. Here we report that NUCB2¹⁻⁸³ promotes osteogenesis. It was found after two months of once-a-day intravenous injection of NUCB2¹⁻⁸³, bone mineral density of femora and lumbar vertebrae were increased in ovariectomized rats. NUCB2¹⁻⁸³ also increased the alkaline phosphatase activity and promoted mineralization in mouse MC3T3-E1 preosteoblastic cell line. When either both Arg⁶⁰ and Arg⁶³ or Ser⁷² were mutated to Ala, the pro-osteogenic activity was completely lost, indicating that these residues are structurally important for its biological function. Furthermore, it encumbered osteoclastic differentiation of RAW 264.7 macrophage. It also excluded any possibility of the effect caused by contaminants or experimental faults, and demonstrated that the pro-osteogenic activity observed was a specific effect of NUCB2¹⁻⁸³ itself. These findings warranted that further studies on NUCB2¹⁻⁸³ would be valuable for the treatment of bone metabolic diseases especially for osteoporosis.