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Accurate prediction of the roughness coefficient of sediment-containing drainage pipes can help engineers optimize urban drainage systems. In this paper, the variation of the roughness coefficient of circular drainage pipes containing different thicknesses of sediments under different flows and slopes was studied by experimental measurements. Back Propagation Neural Network (BPNN) and Genetic Algorithm-Back Propagation Neural Network (GA-BPNN) were used to predict the roughness coefficient. To explore the potential of artificial neural networks to predict the roughness coefficient, a formula based on drag segmentation was established to calculate the roughness coefficient. The results show that the variation trend of the roughness coefficient with flow, hydraulic radius, and Reynolds number is consistent. With the increase of the three parameters, the roughness coefficient decreases overall. Compared to the traditional empirical formula, the BPNN model and the GA-BPNN model increased the determination factors in the testing stage by 3.47 and 3.99%, respectively, and reduced the mean absolute errors by 41.18 and 47.06%, respectively. The study provides an intelligent method for accurate prediction of sediment-containing drainage pipes roughness coefficient. HIGHLIGHTS The relationship between the roughness coefficient and related hydraulic factors is analyzed.; Two intelligent methods (BPNN and GA-BPNN) are used to predict the roughness coefficient.; Based on the concept of drag segmentation, the roughness coefficient calculation formula is established.;

The deposition of sediments with different roughness increases uncertainty in the prediction of effective Manning's n (also known as composite roughness). In this paper, the changing rules of composite roughness and the influence on the discharge capacity of drainage pipes under different flows and slopes were studied by experimental measurement and numerical simulation. When the Froude number is less than 1, the composite roughness decreases gradually with the increase of flow. When the Froude number is more than 1, the composite roughness increases at first and then decreases with the increase of flow. It also showed that the different bed roughness has a great influence on composite roughness, which affects the hydraulic characteristics of flow. The composite roughness of the drainage pipe increases as the bed roughness increases. The water surface rises and the average velocity decreases under a certain slope and flow. The formulas for calculating the composite roughness are obtained by analogy analysis, which can provide theoretical support for the design of the drainage pipeline. HIGHLIGHTS The roughness coefficient is not constant when hydraulic conditions change.; When a sediment deposit is present in a pipe, the overall resistance consists of two components.; The changing rules of composite roughness are related to the flow regime.; The particle size of the sediment in the pipeline will affect the composite roughness.; Bed roughness can be separated from the composite roughness.;

The cross joint is one of the standard connection types for urban water supply pipelines. A pipeline containing cross joints was taken as the research object using NaCl as the tracer. The turbulent mixing characteristics of water and pollutants at the cross joints and pollutant migration and diffusion were studied and analysed by numerical analysis and experimental measurement. The primary purpose is to check the comprehensive influence of the six factors of pipe diameter, inlet flow ratio, outlet flow ratio, location of damage point, flow of brine, and density of brine on the turbulent mixing of water and brine in the pipeline. The coefficient of variation is used as an evaluation index to evaluate the mixing in the pipeline, and the effective mixing length LEML is used to quantify the uniform mixing position of brine in the pipeline. The results show that the inlet flow ratio, outlet flow ratio, and pipe diameter significantly affect LEML in the east outlet of the cross joint. Outlet flow ratio and pipe diameter significantly affect LEML in the cross joint's south outlet direction. In addition, the dimensionless relationship equation representing LEML is fitted through dimensional analysis. HIGHLIGHTS The main and secondary factors affecting the pollutant diffusion at the cross joint were determined by orthogonal test.; The influence of related factors on the turbulent mixing characteristics at the cross joint was studied by using the control variable method.; The dimensionless equation representing the effective mixing length was fitted to quantify the mixing at the cross joint.;

In this randomized control study, we evaluated science teachers' pedagogical practices via classroom observations following ongoing, intensive, and structured instructional support sessions. These sessions included virtual professional development (VPD) and virtual mentoring and coaching (VMC) that accompanied a literacy-infused science curriculum. Using a low-inference observational instrument, we explored the direct impact of VPD and VMC on fifth-grade science teachers' observed time allocation in a randomized controlled trial (RCT) validation study. The observations were collected three times during science instruction from 121 teachers in 68 schools from 35 public school districts in the U.S. state of Texas, during the 2017-2018 school year. Preliminary findings revealed pedagogical differences in time allocation among teachers between treatment and control classrooms. We identified improved instructional practices within treatment classrooms, which suggests the intervention had a positive effect by enhancing the quality of pedagogy as well as the content-area instruction in science.

Abstract Background Understanding how the tumor microenvironment is shaped by various factors is important for the development of new therapeutic strategies. Tumor cells often undergo spontaneous apoptotic cell death in tumor microenvironment, these apoptotic cells are histologically co‐localized with immunosuppressive macrophages. However, the mechanism by which tumor cell apoptosis modulates macrophage polarization is not fully understood. In this study, we aimed to explore the tumor promoting effects of apoptotic tumor cells and the signal pathways involved. Methods Apoptotic cells and macrophages in tumors were detected by immunohistochemical staining. Morphological analysis was performed with Giemsa staining. Lipids generated from apoptotic cells were detected by liquid chromatography‐mass spectrometry. Phosphatidylserine‐containing liposomes were prepared to mimic apoptotic cells. The expression of protein was determined by real‐time PCR, immunohistochemistry enzyme‐linked immunosorbent assay and Western blotting. Mouse malignant ascites and subcutaneous tumor models were designed for in vivo analysis. Transgenic mice with specific genes knocked out and inhibitors specific to certain proteins were used for the mechanistic studies. Results The location and the number of apoptotic cells were correlated with that of macrophages in several types of carcinomas. Phosphatidylserine, a lipid molecule generated in apoptotic cells, induced polarization and accumulation of M2‐like macrophages in vivo and in vitro. Moreover, sustained administration of phosphoserine promoted tumor growth in the malignant ascites and subcutaneous tumor models. Further analyses suggested that phosphoserine induced a M2‐like phenotype in macrophages, which was related to the activation of phosphoserine receptors including T‐cell immunoglobin mucin 4 (TIM4) and the FAK‐SRC‐STAT3 signaling pathway as well as elevated the expression of the histone demethylase Jumonji domain‐containing protein 3 (JMJD3). Administration of specific inhibitors of these pathways could reduce tumor progression. Conclusions This study suggest that apoptotic cell‐generated phosphoserine might be a notable signal for immunosuppressive macrophages in tumors, and the related pathways might be potential therapeutic targets for cancer therapy.

The study of hydraulic characteristics of water flow affected by blocked sediment is vital to assess discharge capacity in pipes with sediment sand to understand the process of pollutant and fine sand aggregation. The aim of this paper is to study the blocking effect of permeable sediments on non-full flow in circular pipe and to analyze hydraulic characteristics with backwater. Experiments and numerical simulations were performed and the porous media model is used to simulate the flow in permeable blocked sediments. To evaluate the degree of congestion, the backwater ratio β is proposed. The backwater ratio is positively proportional to blocked sediment height and inversely proportional to blocked sediment particle size and flow value. Cross-sectional velocity in the backwater zone decreases significantly and tends to be uniform as backwater ratio increases. Furthermore, the development of secondary flows and velocity distributions influenced by backwater height is discussed. The dimensionless shear velocity in backwater zone shows an exponential decrease as the backwater ratio increases, which greatly increases the possibility of further sediment deposition. HIGHLIGHTS The backwater ratio β is proposed to evaluate the effect of blocked sediment and to study the relationship between backwater height and blockage parameters.; The porous media model is used to simulate the flow in permeable blocked sediments.; The changes of flow field are observed under different backwater ratio.;

Ovarian clear cell carcinoma (OCCC) is a rare subtype of epithelial ovarian cancer with unique molecular characteristics, specific biological and clinical behavior, poor prognosis and high resistance to chemotherapy. Pushed by the development of genome-wide technologies, our knowledge about the molecular features of OCCC has been considerably advanced. Numerous studies are emerging as groundbreaking, and many of them are promising treatment strategies. In this article, we reviewed studies about the genomics and epigenetics of OCCC, including gene mutation, copy number variations, DNA methylation and histone modifications.

The main objective of this study is to revise and validate the assessment of self-presentation skills of middle school students. The assessment is based on existing self-assessment scales and adaptively modified for a more accurate assessment of middle school students' self-presentation skills. Considering the characteristics of middle school students and teachers' assessment, we have reconstructed and adjusted the dimensions of the original assessment. This paper includes background research, scale design and innovation, scale testing, and scale reliability and multiple validity verification. In order to further verify the effectiveness and reliability of the tool, this study added the application of content validity and factor analysis on the basis of previous studies. The results of this study are intended to compensate for the limitations of current teacher assessment for middle school students' self-presentation. At the same time, this study also provides a basis for the development of middle school students' self-presentation skills integrated training programs.

Field-based practicum experiences are essential in connecting classroom theory to the real world. In this mixed methods study, we analyzed principal candidates' perceptions of their summer residency practicum experiences via a self-assessment principal leadership competency instrument and virtual semi-structured interviews. We found that principal candidates expressed leadership growth in the following Texas principal leadership competency areas: Strategic Operations, Human Capital, and Executive Leadership. These findings were consistent in our quantitative and qualitative data analysis. Conversely, the principal candidates stated that their greatest leadership challenges during the summer residency practicum involved providing instructional feedback and engaging in difficult conversations. The results of this study may assist principal preparation programs in revamping their curriculum to include a more meaningful residency-practicum experience.

This study examined the effects of an integrated approach that combines segmentation and self-explanation designs on learner achievement for meaningful video-based learning. This was a pretest-posttest research design with a sample size of 121 participants randomly assigned to one of four different types of video instructions (continuous, segmentation, self-explanation, or the combination of segmentation and self-explanation). Participants engaged in video instruction that used either a continuous video or segmented video clips and incorporated either self-explanation prompts or no self-explanation prompts. The results showed that participants in the combination and segmentation conditions outperformed those in the continuous condition in evaluation ability, and participants in the combination condition outperformed those in the continuous condition in the overall performance after controlling for prior knowledge. The current study indicates that the combined design can effectively facilitate student learning by engaging them in meaningful video-based learning.

Background: To identify endometrial mesenchymal stem cells (eMSCs) in retrograde menstruation, in various endometriosis lesions, in normal control tissues, and to investigate the association between eMSCs and endometriosis. We also plan to evaluate the effect of gonadotropin-releasing hormone agonists (GnRH-a) on eMSCs. Methods: Patients diagnosed with endometriosis were included if they had experienced surgery during the time frame 1 January 2015 to 31 December 2019 in West China Second Hospital, Sichuan University. Immunofluorescence was performed to identify eMSCs in those tissues with cell surface markers PDGFR-β/CD146. The percents of eMSCs in various tissues were calculated, and compared using analysis of variance. A two-sided pvalue less than 0.05 showed significant difference. Results: This study included 508 patients. eMSCs were identified in retrograde menstruation and numerous pathologic specimen but were not detected in normal control tissues. There was no significant difference in the percent of eMSCs between the GnRH-a treatment group and the control group (p > 0.05). Conclusions: Our study demonstrated that eMSCs played a critical role in the development and recurrence of endometriosis and that GnRH-a did not affect eMSCs. Gynecologists should regard endometriosis as a chronic disease requiring lifetime management, especially for patients with chronic pelvic pain.

The ability of children to ask curiosity-driven questions is an important skill that helps improve their learning. For this reason, previous research has explored designing specific exercises to train this skill. Several of these studies relied on providing semantic and linguistic cues to train them to ask more of such questions (also called "divergent questions"). But despite showing pedagogical efficiency, this method is still limited as it relies on generating the said cues by hand, which can be a very long and costly process. In this context, we propose to leverage advances in the natural language processing field (NLP) and investigate the efficiency of using a large language model (LLM) for automating the production of key parts of pedagogical content within a curious question-asking (QA) training. We study generating the said content using the "prompt-based" method that consists of explaining the task to the LLM in natural text. We evaluate the output using human experts annotations and comparisons with hand-generated content. Results suggested indeed the relevance and usefulness of this content. We then conduct a field study in primary school (75 children aged 9-10), where we evaluate children's QA performance when having this training. We compare 3 types of content: (1) hand-generated content that proposes "closed" cues leading to predefined questions; (2) GPT-3-generated content that proposes the same type of cues; (3) GPT-3-generated content that proposes "open" cues leading to several possible questions. Children were assigned to either one of these groups. Based on human annotations of the questions generated, we see a similar QA performance between the two "closed" trainings (showing the scalability of the approach using GPT-3), and a better one for participants with the "open" training. These results suggest the efficiency of using LLMs to support children in generating more curious questions, using a natural language prompting approach that affords usability by teachers and other users not specialists of AI techniques. Furthermore, results also show that open-ended content may be more suitable for training curious question-asking skills.

Knowledge building (KB) competencies are crucial for undergraduates' creative knowledge work and academic success. While there is substantial research on KB discourse, there are limited efforts in examining how KB competencies in the conceptual, metacognitive, socio-emotional, and epistemic dimensions are demonstrated in KB discourse and how the competencies can be scaffolded. Previous studies suggest the effectiveness of reflective assessment on sustainable and productive KB discourse. This study developed a framework for analyzing KB competencies using KB discourse moves. It also examined whether a KB design augmented by reflective assessment enriched by analytic tools and artifacts could foster undergraduates' KB competencies, and if so, how. This KB design involves principle-based pedagogy with the participants engaging in collaborative inquiry and discussion on Knowledge Forum, and reflective assessment using (a) super synthesis notes, (b) KB interaction rubrics, and (c) learning analytics visualization tools. Qualitative tracking and lag sequential pattern analysis of Knowledge Forum's discourse revealed that implementing reflective assessment supported by analytics and artifacts could help the undergraduate students develop KB competencies manifested in discourse with evidence of conceptual advance, epistemic engagement, metacognition, and productive socio-emotional interactions with a collective focus. The thematic analysis illustrated the dynamics through which the design enriched by standards and visualizations helped the undergraduates develop KB competencies: leveraging synthesis super notes to promote conceptual and metacognitive advancement and epistemic engagement; employing KB interaction rubrics to cultivate metacognitive, socio-emotional, and epistemic competencies; and harnessing KBDeX visualizations to promote metacognitive and conceptual advancement and to facilitate epistemic engagement. The implications of scaffolding students' epistemic agency, metacognition, productive collaborative inquiry, and developing KB competencies in a technology-supported metacognitive learning environment are discussed.

This study applies an adapted Tripartite Integration Model of Social Influences (TIMSI) framework to investigate the socialization experiences of undergraduate students participating in the STEM Scholars Program (SSP), with a particular focus on the relationship between first-generation status and STEM socialization. A sample of N = 193 students was longitudinally tracked and surveyed on five occasions throughout their four-year college journey, ranging from the summer bridge to the end of their four academic college years. Through the application of latent growth curve modeling, we identified quadratic growth trajectories in three indicators of social influence/socialization processes (i.e., scientific self-efficacy, scientific identity, and SSP program values), as well as a co-development between social influence processes and scientific integration. Notably, the study finds that first-generation students report comparable growth rates of socialization experiences to their continuing-generation peers. These results suggest that the SSP may help bridge the gap for first-generation students as they embark on their journey into STEM fields. The implications of this study highlight the importance of socialization in STEM education, as well as the recruitment of first-generation students in STEM intervention programs.

The application of artificial intelligence in physical education (AIPE) has provided new ways to improve learning and teaching activities in physical classes. However, literature reviews that provide a systematic review and analysis of AIPE are limited. To address this gap, this study provided an overview of AIPE-related empirical research. Specifically, it examined the general state of AIPE, algorithms used for AIPE, and the impact and challenges of AIPE. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 130 empirical studies related to AIPE were included in the final synthesis. The findings of this study demonstrated that numerous studies have explored the use of AI technologies to enhance physical education classes and training processes. These technologies have been widely employed in athletic performance analysis, health monitoring, and personalized training. AIPE offered great potential for providing personalized instruction, real-time feedback and assessment, and diverse learning environments. However, the use of AI technology poses challenges, including technical reliability and accuracy, privacy and security issues, as well as technical training and teacher support. These findings provide insights for future research on AIPE.

The programme outcomes are broad statements of knowledge, skills, and competencies that the students should be able to demonstrate upon graduation from a programme, while the Educational Taxonomy classifies learning objectives into different domains. The precise mapping of a course outcomes to the programme outcome and the educational taxonomy (Cognitive, Psychomotor and Affective) level is crucial to ensure Constructive Alignment at the fundamental level of a course and to ensure meaningful outcome measurements. Unfortunately, this effort is often subject to bias and human error while the use of information technologies as a mediator in this area remains unexplored. This research paper proposes an automatic learning-based advisory system for engineering curriculum to ensure constructive alignment with programme outcomes and educational taxonomy. We demonstrated the use of natural language processing and machine learning techniques to mitigate human error and bias that is often present in such classification tasks. Textual/semantic embeddings, including Term Frequency-Inverse Document Frequency (TF-IDF), Universal Sentence Encoder (USE), and Word2Vec (W2V), machine learning models (Random Forest, Support Vector Machine, Logistic Regression, and Light Gradient Boosting Machine), and their corresponding techniques for optimizing the training process are extensively investigated. In terms of accuracy, we obtained an encouraging result of 78.83%, and 78.71% for TF-IDF with Random Forest, and USE with Support Vector Machine classifier, respectively. We transformed our work into a web-based solution named the Course Outcomes Diagnostic Tool, embedded in the faculty education web platform, Edu Centre that is ubiquitously adopted by the members in the Faculty of Engineering and Technology, Tunku Abdul Rahman University of Management and Technology. The proposed solution has demonstrated great potential in reducing subjectivity, ambiguity, and human error, thereby improving the constructive alignment at the root level of course design to ensures teaching-learning activities are aligned with regulatory body expectations.

In this study, we examine the design process for a visual learning analytics (VLA) approach in addition to its effect on developing students' epistemic understanding of discourse and relationships to promote knowledge-building inquiry. Over a 3-month period, an experimental class of secondary school students took a visual arts course using a knowledge-building design augmented by VLA, while a comparison class took the same course within a regular knowledge-building environment. The VLA approach implemented in the experimental class emphasized collective reflections using assessment tools to visualize the students' online and classroom discussions. The analysis of the students' questionnaire responses and Knowledge Forum discourse showed that the VLA approach helped the students develop a deeper epistemic understanding of discourse for productive inquiry and greater domain knowledge. In addition, the students' epistemic understanding was associated with their performance in productive Knowledge Forum inquiry. Qualitative analysis of the classroom and group discussions, interview responses and learning artefacts showed that the VLA design helped them understand the nature of discourse deeply by encouraging them to notice community orientations, increase their awareness of productive discourse moves and advance idea development. The implications of the VLA design in improving epistemic understanding of discourse are discussed. We contribute to the literature by examining the epistemic dimensions of discourse and implementing VLA as an epistemic tool for knowledge building.

Understanding the teacher's technology adoption process is essential to comprehend and narrow the digital divide in the post-epidemic age. During the pandemic, the stay-at-home orders not only intervened schooling and teaching but also increased digital accessibility to teachers. This research studies teacher heterogeneity and adoption controls in the epidemic to simultaneously affect teacher's underlying intention and adoption behavior based on a dynamic framework under the theory of planned behavior. We present a quantitative framework for modeling the teachers' adoption behavior of a technology conditioned on intention, defined as latent dynamic processes via a hidden Markov model. This model allows us to examine the effects of three concerned adoption controls: epidemic, community, and experience. We also explicitly characterized teachers' digital traits as the estimated results accounts for teacher's heterogeneity. The implicit quality of digital teaching artifacts is examined to correlate the dynamic analyses with the qualitative supports. We collected data from four primary schools in Shanghai over 173 weeks, using an after-school activity management system. The data collection spanned periods both before and after the school closure caused by the epidemic, providing us with a dynamic view of technology adoption patterns under different circumstances. Our results suggest that the interventions derived from the controls of the epidemic did not significantly narrow the digital gap. In particular, well-prepared teachers may be more sensitive to adjusting their usage to meet the evolving standards. The inexperienced teacher struggles to maintain a high level of adoption once the passive external pressure is eliminated. Even the compulsory policy can temporarily change their adoption behavior. These implications highlight the second-order digital divide problem.

Statistical learning enables humans to involuntarily process and utilize different kinds of patterns from the environment. However, the cognitive mechanisms underlying the simultaneous acquisition of multiple regularities from different perceptual modalities remain unclear. A novel multidimensional serial reaction time task was developed to test 40 participants' ability to learn simple first-order and complex second-order relations between uni-modal visual and cross-modal audio-visual stimuli. Using the difference in reaction times between sequenced and random stimuli as the index of domain-general statistical learning, a significant difference and dissociation of learning occurred between the initial and final learning phases. Furthermore, we used a negative and positive occurrence-frequency-and-reaction-time correlation to indicate implicit and explicit learning, respectively, and found that learning simple uni-modal patterns involved an implicit-to-explicit segue, while acquiring complex cross-modal patterns required an explicit-to-implicit segue, resulting in a X-shape crossing of regularity learning. Thus, we propose an X-way hypothesis to elucidate the dynamic interplay between the implicit and explicit systems at two distinct stages when acquiring various regularities in a multidimensional probability space.

BACKGROUND: Evidence suggested that abiotic airborne exposures may be associated with changes in body composition. However, more evidence is needed to identify key pollutants linked to adverse health effects and their underlying biomolecular mechanisms, particularly in sensitive older adults. OBJECTIVES: Our research aimed to systematically assess the relationship between abiotic airborne exposures and changes in body composition among healthy older adults, as well as the potential mediating mechanisms through the serum lipidome. METHODS: From September 2018 to January 2019, we conducted a monthly survey among 76 healthy adults (60-69 years old) in the China Biomarkers of Air Pollutant Exposure (BAPE) study, measuring their personal exposures to 632 abiotic airborne pollutions using MicroPEM and the Fresh Air wristband, 18 body composition indicators from the InBody 770 device, and lipidomics from venous blood samples. We used an exposome-wide association study (ExWAS) and deletion/substitution/addition (DSA) model to unravel complex associations between exposure to contaminant mixtures and body composition, a Bayesian kernel machine regression (BKMR) model to assess the overall effect of key exposures on body composition, and mediation analysis to identify lipid intermediators. RESULTS: The ExWAS and DSA model identified that 2,4,5-T methyl ester (2,4,5-TME), 9,10-Anthracenedione (ATQ), 4b,8-dimethyl-2-isopropyl-phenanthrene, and 4b,5,6,7,8,8a,9,10-octahydro-(DMIP) were associated with increased body fat mass (BFM), fat mass indicators (FMI), percent body fat (PBF), and visceral fat area (VFA) in healthy older adults [Bonferroni-Hochberg false discovery rate ([FDR.sub.BH]) < 0.05]. The BKMR model demonstrated a positive correlation between contaminants (anthracene, ATQ, copaene, di-epi-a-cedrene, and DMIP) with VFA. Mediation analysis revealed that phosphatidylcholine [PC, PC(16:1e/18:1), PC(16:2e/18:0)] and sphingolipid [SM, SM(d18:2/24.1)] mediated a significant portion, ranging from 12.27% to 26.03% (p-value DISCUSSION: Based on the evidence from multiple model results, ATQ and DMIP were statistically significantly associated with the increased VFA levels of healthy older adults, potentially regulated through lipid intermediators. These findings may have important implications for identifying potentially harmful environmental chemicals and developing targeted strategies for the control and prevention of chronic diseases in the future, particularly as the global population is rapidly aging. https://doi.org/10.1289/EHP13865, Introduction The global population is currently undergoing a notable aging trend, primarily driven by declining fertility rates and increased life expectancy. (1) This demographic shift is evident as the number [...]