Abstract A deep-water offshore oil field belongs to a carbonate reservoir with a depth of over 2000 meters, and is developed using large well spacing water gas alternating (WAG) flooding. Compared with onshore oilfields, single well investment in deepwater oilfields is high, and in order to achieve the goal of high production with thin wells, the requirements for well location deployment are higher. At the same time, the presence of heterogeneity in carbonate reservoirs further increases the difficulty of well layout. Therefore, optimizing the well location for the development of water gas alternative flooding in deep water carbonate reservoirs to achieve optimal cumulative production and economic benefits is a challenge. Optimization of well locations based on numerical simulation usually requires engineers to spend a lot of time and energy. With the rapid development of artificial intelligence (AI) technology, using machine learning algorithms to optimize well locations may be a fast and reliable solution. The research started with data processing. Firstly, data related to well location optimization parameters are collected and pre-processed, such as data filling and data normalization. Pearson algorithm is used to judge the importance of feature parameters according to correlation, and finally the interference between features is reduced by dimensionality reduction algorithm. After data processing, a reservoir agent model is established based on XGBoost (eXtreme Gradient Boosting), the Sparrow Search Algorithm (SSA) is used to optimize the hyperparameters of the model, and SSA-XGBoost is used to optimize the well location of deepwater carbonate reservoirs in multiple rounds. The results clearly show that AI is a powerful tool for optimizing well placement in deepwater carbonate reservoirs. SSA-XGBoost model scored 0.99 in the training set decision coefficient, 0.96 in the test set decision coefficient, and 0.83 in the verification set decision coefficient, which has higher prediction accuracy compared with other machine learning algorithms. This study provides a technical method for the location optimization of WAG flooding Wells in deep water carbonate reservoirs

Abstract Water gas alternate flooding is a successful oil recovery method applied in mining fields. Compared with conventional water gas alternate flooding in onshore oilfields, the use of water gas alternate flooding in deepwater oilfields has certain peculiarities. Due to limited gas export in deep-water oil fields, in order to improve oil recovery and meet environmental requirements, it is necessary to inject produced gas back into the ground, using a water gas alternating drive method based on the circulation of produced gas. However, there is little research on the optimization of injection and production parameters for water and gas alternate flooding in deepwater oil fields. This article conducts long core displacement experiments based on the geological characteristics and fluid properties of the studied deepwater oil fields, and compares the effects of three displacement methods: water injection, continuous gas drive, and water gas alternate drive. Based on the results of long core experiments and PVT fitting, a mechanism model for the development of water gas alternating mixed phase flooding at the field scale was established. The optimization of injection and production parameters, such as water injection timing, pressure maintenance level, water gas ratio, injection production ratio, and water gas alternation cycle, was carried out through numerical simulation in deepwater oil fields. The results of this study can provide a reference basis for optimizing injection and production parameters for the development of water and gas alternation in deepwater oil fields.

: Over the past 40 years since the comprehensive implementation of family planning, the rapid population growth has been effectively controlled and the population has been regenerated. The historic change in production type has effectively eased the pressure on resources and the environment, and effectively promoted economic development.This paper uses the population development data of comprehensively evaluating the population development status of Anhui province in 2020.Through the Anhui Statistical Yearbook, 10 indicators, including birth rate, death rate, illiteracy rate, enrollment rate, dependency rate, unmarried rate, dependency ratio, divorce rate and widowed rate, were collected.After data preprocessing, the data set was systematically clustered by Matlab software, and the quality of Anhui population development was comprehensively evaluated by principal component analysis (PCA).Through the analysis, we find that the top three cities are Bozhou, Fuyang and Suzhou.And provide some reference suggestions for the population development of each city.

The Positive and Negative Affect Schedule (PANAS) is the most widely used self-report instrument for assessing affect. However, there are inconsistent findings regarding the factor structure of the PANAS. In this study, we applied Bayesian structural equation modeling (BSEM) to investigate the structure of the PANAS using data from a sample of 893 Chinese middle and high school students. Four models, the orthogonal two-, the oblique two-, the three-, and the bi-factor models were tested with prior specifications including approximately zero cross-loadings and residual covariances. The results indicated that the orthogonal two-factor model specified with informative priors for both cross-loadings and residual correlations has the best model fit. Confirmatory factor analysis with the maximum likelihood estimator (ML-CFA) based on modifications from BSEM analysis showed improved model fit compared to ML-CFA based on frequentist analysis, which is the evidence for the merit of BSEM for addressing misspecifications.

In educational and psychological research, it is common to use latent factors to represent constructs and then to examine covariate effects on these latent factors. Using empirical data, this study applied three approaches to covariate effects on latent factors: the multiple-indicator multiple-cause (MIMIC) approach, multiple group confirmatory factor analysis (MG-CFA) approach, and the structural equation model trees (SEM Trees) approach. The MIMIC approach directly models covariate effects on latent factors. The MG-CFA approach allows testing of measurement invariance before latent factor means could be compared. The more recently developed SEM Trees approach partitions the sample into homogenous subsets based on the covariate space; model parameters are estimated separately for each subgroup. We applied the three approaches using an empirical dataset extracted from the eighth-grade U.S. data from the Trends in International Mathematics and Science Study 2019 database. All approaches suggested differences among mathematics achievement categories for the latent factor of mathematics self-concept. In addition, language spoken at home did not seem to affect students’ mathematics self-concept. Despite these general findings, the three approaches provided different pieces of information regarding covariate effects. For all models, we appropriately considered the complex data structure and sampling weights following recent recommendations for analyzing large-scale assessment data.

Purpose: The diagnosis of Autism Spectrum Disorder (ASD) occurs in one in 54 children and companion animals (CA) are common in families of children with ASD. Despite evidence of CA ownership benefits for children with ASD, little is known about cats. The purpose was to explore the impact of shelter cat adoption by families of children with ASD. Design and methods: This was the first randomized controlled trial of adoption of a temperament screened cat by families of children with ASD. Families assigned to the treatment group adopted a cat and were followed for 18 weeks. Families assigned to the control group were followed for 18 weeks without intervention, then converted to treatment, by adopting a cat and were followed another 18 weeks. Adopted cats were screened using the Feline Temperament Profile to identify a calm temperament. Surveys measured children's social skills and anxiety and parent/child cat bonding. Results: Our study (N=11) found cat adoption was associated with greater Empathy and less Separation Anxiety for children with ASD, along with fewer problem behaviors including Externalizing, Bullying and Hyperactivity/Inattention. Parents and children reported strong bonds to the cats. Conclusion: This exploratory study found introduction of a cat into the home may have a positive impact on children with ASD and their parents. Based on this intial finding, future studies with larger sample sizes are recommended. Practice implications: If parents of children with ASD are considering cat adoption, health care providers might consider recommending adoption of a cat screened for calm temperament. (C) 2020 Elsevier Inc. All rights reserved.

Background: Cats are a common companion animal (CA) in US households, and many live in families of children with autism spectrum disorder (ASD). The prevalence of ASD is one in 54, and many children have behavior challenges as well as their diagnostic communication disorders. Objective: Benefits of CAs for children with ASD have been identified, but little is known about the welfare of CAs in these homes. This study explored the welfare of cats ( N = 10) screened for ideal social and calm temperament using the Feline Temperament Profile (FTP) and adopted by families of children with ASD. Methods: Cat stress was measured using fecal cortisol, weight, and a behavior stress measure (cat stress score). Measures were taken at baseline in the shelter, 2–3 days after adoption, and at weeks 6, 12, and 18. Result: Outcome measures suggested the adopted cats' stress levels did not increase postadoption; however, the small sample size limited analytical power and generalizability. Conclusion: This study provides preliminary evidence for the success of cat adoption by families of children with ASD, when cats have been temperament screened and cat behavior educational information is provided. Further research is warranted to confirm these findings.

The simple view of writing suggests that written composition results from oral language, transcription (e.g., spelling/handwriting), and self-regulation skills, coordinated within working memory. The model provides a number of implications for the interpretation of psychoeducational achievement batteries. For instance, it hypothesizes that writing skills are only partially related to each other through a hierarchy of levels of language (e.g., subword, word, sentence, discourse levels) and that transcription skills such as spelling mediate the effects of language skills on composition. We evaluated implications of the simple view of writing in the Wechsler Individual Achievement Test, 3rd Edition (WIAT-III). Using structural equation modeling, we established that WIAT-III writing tasks are only partially related to each other within both the battery’s normative sample and an independent sample of students referred for special education. We also described how lower level writing skills mediated the effects of language skills on higher level writing skills. However, these effects varied across normative and referral samples.

The study goal was to explore companion animal (CA) ownership in families of children with autism spectrum disorder (ASD), including parents’ beliefs about benefits and burdens of CAs, as well as parent stress. Participants (N = 764) completed online survey instruments anonymously. Findings revealed that parents with lower incomes perceived more benefits of CAs and their children were more strongly bonded with their CAs. Parents owning both a dog and cat perceived more benefits than those with only a dog or cat. Dog owners perceived more benefits than cat owners. Parents who perceived their CAs as providing more benefits had less stress. Provider implications are to consider recommending CAs to families of children with ASD for family benefits including lower parental stress.

The programming language of R has useful data science tools that can automate analysis of large-scale educational assessment data such as those available from the United States Department of Education’s National Center for Education Statistics (NCES). This study used three R packages: EdSurvey, MplusAutomation, and tidyverse to examine the big-fish-little-pond effect (BFLPE) in 56 countries in fourth grade and 46 countries in eighth grade for the subject of mathematics with data from the Trends in International Mathematics and Science Study (TIMSS) 2015. The BFLPE refers to the phenomenon that students in higher-achieving contexts tend to have lower self-concept than similarly able students in lower-achieving contexts due to social comparison. In this study, it is used as a substantive theory to illustrate the implementation of data science tools to carry out large-scale cross-national analysis. For each country and grade, two statistical models were applied for cross-level measurement invariance testing, and for testing the BFLPE, respectively. The first model was a multilevel confirmatory factor analysis for the measurement of mathematics self-concept using three items. The second model was multilevel latent variable modeling that decomposed the effect of achievement on self-concept into between and within components; the difference between them was the contextual effect of the BFLPE. The BFLPE was found in 51 of the 56 countries in fourth grade and 44 of the 46 countries in eighth grade. The study provides syntax and discusses problems encountered while using the tools for modeling and processing of modeling results.

The research describes efforts toward developing a valid and reliable scale used to assess science communication training effectiveness (SCTE) undertaken in conjunction with a 4-year project funded by the National Science Foundation. Results suggest that the SCTE scale possesses acceptable psychometric properties, specifically reliability and validity, with regard to responses from graduate students in science, technology, engineering, and math fields. While it cannot be concluded that the SCTE scale is the “be-all-end-all” tool, it may assist investigators in gauging success of science communication training efforts and by identifying aspects of the program that are working or that need improving.

The Wechsler individual achievement test, third edition (WIAT-III) is a popular individually administered achievement battery. Despite its ubiquity in assessment practice, scant research into its structure exists. We analyzed the structure of the WIAT-III in a sample of students in Grades 3-12 referred for special education evaluations (n = 355). Using confirmatory factor analysis, we evaluated the fit of the factor structure assumed by the composite scores provided by the test publisher. We then compared that model to alternative first-order, second-order and bifactor models in an exploratory fashion. Results demonstrated that the publisher implied model does not capture the structure of the WIAT-III. Instead subtests appear related to each other based on both their domain of academic performance and in other shared abilities. Implications for practice are discussed.

AbstractThis study investigates the co-teaching practices implemented in Chinese language teaching in middle schools and high schools in a school district in the Midwestern United States. With the overarching question of how co-teaching with a native speaker teacher and a language expert teacher enhances the teaching and learning process of Chinese, this study examines co-teachers’ past experiences, their roles and experiences in the co-taught Chinese classes, and their perceptions of student learning and of partner teachers’ experiences, as well as students’ motivational perceptions and classroom engagement. A mixed-methods approach is used. Results suggest that some of the challenges in the co-teaching program are due to insufficient previous co-teaching experience, Chinese co-teachers’ unfamiliarity with the U.S. classroom, and lack of clarity regarding the co-teachers’ responsibilities. The co-teaching approach used in this program is “one teach, one assist.”

Establishing measurement invariance has been emphasized as an important scale validation procedure for group comparisons. The 28-item Career Futures Inventory–Revised (CFI-R) is a widely used measure of career adaptability that has demonstrated initial validity with various samples. The purpose of the present study is to further examine the validity of the CFI-R by testing measurement invariance between a general university student sample and a client sample. First, a five-factor confirmatory factor analysis model was tested with each group. Then, measurement invariance tests were conducted through subsequently examining configural invariance, metric invariance, and scalar invariance. Test of invariance was achieved until partial scalar invariance, suggesting that the CFI-R is similarly applicable to both clinical and nonclinical samples. In addition, the comparisons of latent means between two groups revealed that clients showed significantly lower latent means than general students for four factors: Career Agency, Occupational Awareness, Support, and Work–Life Balance.

The authors describe a science communication training called Decoding Science and the steps taken to develop and assess program effectiveness. Evaluation is based on a triangulated framework involving feedback from graduate student trainees, faculty trainers, and ordinary citizens who are not specialists in the field. Three cohorts of graduate STEM (science, technology, engineering, math) students participated in the training in Spring 2016 (N = 18), Fall 2016 (N = 11), and Spring 2017 (N = 14). Analysis of these evaluations indicates significant improvements in trainees' communication of science. We conclude that our triangulated approach can be useful in science communication training.

Professional learning communities (PLCs) promote collaboration among school personnel in an effort to stimulate student learning. Using data obtained from a larger statewide initiative in Missouri, the current study examined data from 181 schools (102 elementary schools, 32 middle schools, 41 high schools, and 6 other schools, average of 428.76 students) to determine (a) the factors that can be used to assess the effects of PLCs, (b) how well PLCs relate to student achievement, and (c) the extent to which teams differentially implement the factors. An exploratory factor analysis and confirmatory factor analyses resulted in two broader constructs that represented PLC attributes, collaborative leadership process and data-driven systems for learning, both of which correlated with student achievement and together provided unique variance in mathematics beyond school variables and achievement scores from before the PLC began. Directions for future research and implications for practice are discussed.

This study investigated academic norms related to faculty engagement with industry in one public research-intensive university in the United States in the fields of Science, Technology, Engineering, Mathematics, and Medical (STEMM). Primary methods included exploratory and confirmatory factor analysis, logistic regression, and ANOVA applied to responses to a survey instrument we implemented based on current literature on university/industry linkages. Results show how faculty embrace the coexistence of different logics in their norms differing by rank, type of engagement with industry, prior experience working in the industry, and field. Overall, faculty in this study hold values traditionally associated with the academic profession except when there are possibilities of participating in the market and seek commercial gain from research.

In this article, we analysed the use of surveys conducted by the United States' National Center for Education Statistics (NCES) related to postsecondary education in studies published in 2015. We discuss topics studied, methods used and limitations reported. Based on the review of the 27 published articles in 2015, we found that the most commonly used NCES postsecondary surveys are ELS:2002 and IPEDS, followed by BPS:04/09, NPSAS, NELS:1988, B&B:1993/1997/2003 and HSLS:2009. The issues studied in the articles reviewed include college access and choice, student outcomes and higher education finances. In addition, our analysis indicates that these articles applied appropriate and advanced analytical methods and the majority of them took into consideration the complex sampling designs and data structures of these NCES surveys. We concluded with a series of recommendations for both users and leaders developing these surveys in order to maximise their utility. These recommendations, if adopted, will undoubtedly result in more use of NCES data for research.