On the Feasibility of the Chinese County-Level Convergence Media Centers in Participating in International Communication:Evidence from CDHT’s First-Hand Communication Practice

With the advent of new technologies,China is faced with the practical need to enhance its international communication capacity and address reforms in the modes,content,and channels of international communication.Therefore,more efforts should be made to further enrich the participants in international communication by optimizing China’s four-level journalism communication system and crafting a“great external publicity pattern.”Guiding and promoting county-level convergence media centers to participate in international communication is conducive to promoting the building of a vertical international communication system and a multi-level integrated communication matrix and facilitating the resource alignment and information sharing between domestic and international communication.In practice,some county-level convergence media centers in China have achieved certain results in international communication.A good example is the Chengdu Hi-tech Industrial Development Zone(CDHT),a pioneer in the reform of Chinese county-level convergence media.Therefore,this paper has used CDHT as an example to discuss the reality and basic conditions for Chinese county-level convergence media centers to participate in international communication and put forward general paths for county-level convergence media centers to engage in international communication.

A Machine Learning-Based Web Application for Heart Disease Prediction

This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.

Self-access Centers’Effects on College English Learning

This paper aims to investigate self-access centers'effects on college English learning. Data, collected in the form of questionnaires and interview, were analyzed. Results demonstrate that the self-access center(SAC)does help students to promote learner autonomy, lower their anxiety and encourage their interest in college English learning.

Ensemble learning prediction of soybean yields in China based on meteorological data

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

Application of Student-centered Learning Approach to English Listening and Speaking Class in Vocational Schools

Student-centered learning approach is focused on the students' demands and interests.Applying student-centered approach puts forward higher requirement to English teachers.This article first analyzes the theory of student-centered learning approach and compares teacher-centered approach with it.Based on the research information and teaching experience,the author summarizes four strategies about how to apply student-centered learning approach to English listening and speaking class in vocational schools.

Correlation between Geographical Regional Economy and Construction of County-level Vocational Education Centers

In the period of Eleventh Five-Year Plan,the construction of county-level vocational education centers in Shanxi Province made great contribution to local social and economic development. Due to influence of special geography,population environment and regional economic development situation,it forms the pattern of strong in southeast and weak in northwest. Thus,scientific making plan for construction of vocational education centers in northwestern counties( cities) and establishing long-term mechanism for construction of vocational education centers will be helpful for serving regional economic construction.

SANAA的转身——比较法视点下劳力士研修中心(Rolex Learning Center)之“变”

本文从分析Rolex Learning Center的创作背景入手,透视出其在"Super Flat"思潮影响下的建筑手法与反映、表现"不确定性"的建筑观。基于上述理论比较近期几个作品,讨论SANAA提供新建筑形制和体验的思考过程,揭示了"不确定性"在SANAA和库哈斯间的交集与差异,以及SANAA手法之变背后恒定不变的价值观。

Learners＇ Autonomy in Self-access Centers——An Exploration of the SAC in Chongqing University

This paper mainly focuses on university-level learners＇ autonomy in computer-assisted self-access English learning centers through exploring the self-access center in Chongqing University from three aspects： the general survey, the function and the problems of the self-access center.

The Problems and its Countermeasures of Autonomous Language Learning Center

With the popularity of Computer Assisted Language Learning(CALL),autonomous language learning centers have been established in the universities throughout the country.However,there are many problems exist in the practice of the autonomous language learning center.This essay tries to discuss the problems and its countermeasures with an aim of improving College English learning and teaching by the example of autonomous language learning center in Nanyang Institute of Technology.

Context-aware reinforcement learning for cooling operation of data centers with an Aquifer Thermal Energy Storage

Data centers are often equipped with multiple cooling units. Here, an aquifer thermal energy storage (ATES) system has shown to be efficient. However, the usage of hot and cold-water wells in the ATES must be balanced for legal and environmental reasons. Reinforcement Learning has been proven to be a useful tool for optimizing the cooling operation at data centers. Nonetheless, since cooling demand changes continuously, balancing the ATES usage on a yearly basis imposes an additional challenge in the form of a delayed reward. To overcome this, we formulate a return decomposition, Cool-RUDDER, which relies on simple domain knowledge and needs no training. We trained a proximal policy optimization agent to keep server temperatures steady while minimizing operational costs. Comparing the Cool-RUDDER reward signal to other ATES-associated rewards, all models kept the server temperatures steady at around 30 °C. An optimal ATES balance was defined to be 0% and a yearly imbalance of −4.9% with a confidence interval of [−6.2, −3.8]% was achieved for the Cool 2.0 reward. This outperformed a baseline ATES-associated reward of 0 at −16.3% with a confidence interval of [−17.1, −15.4]% and all other ATES-associated rewards. However, the improved ATES balance comes with a higher energy consumption cost of 12.5% when comparing the relative cost of the Cool 2.0 reward to the zero reward, resulting in a trade-off. Moreover, the method comes with limited requirements and is applicable to any long-term problem satisfying a linear state-transition system.

On Improving the Classroom Environment of English Learning in College

This paper, firstly, acknowledges the importance of classroom environment and the problems existing in the college English classroom. And then, it offers some ways of improving the classroom environment which is very critical to evaluate educational programs and curriculum and provides guidance to teachers who are eager to boost their classroom teaching.

An Experimental Study on the Development of Self-access Language Learning by Non-English Majors

Self-access language learning has attracted much attention in second language teaching and researching. This paper aims to do some researches on developing self-access language learning in the self-access center and test its effect on developing learner autonomy. Data,collected in the form of questionnaires and interview,were analyzed. Results show the development of self-access language learning by non-English majors.

Engagement in clinical learning environment among nursing students: Role of nurse educators

Student engagement in a clinical learning environment is a vital component in the curricula of pre-licensure nursing students, providing an opportunity to combine cognitive, psychomotor, and affective skills. This paper is significant in Arab world as there is a lack of knowledge, attitude and practice of student involvement in the new clinical learning environment. The purpose of this review article is to describe the experiences and perspectives of the nurse educator in facilitating pre-licensure nursing students’ engagement in the new clinical learning environment. The review suggests that novice students prefer actual engagement in clinical learning facilitated through diversity experiences, shared learning opportunities, student-faculty interaction and active learning. They expressed continuous supervision, ongoing feedback, interpersonal relationship and personal support from nurse educators useful in the clinical practice. However, the value of this review lies in a better understanding of what constitutes quality clinical learning environment from the students’ perspective of engagement in evidence-based nursing, reflective practice, e-learning and simulated case scenarios facilitated by the nurse educators. This review is valuable in planning and implementing innovative clinical and educational experiences for improving the quality of the clinical teaching-learning environment.

Deep Reinforcement Learning Model for Blood Bank Vehicle Routing Multi-Objective Optimization

The overall healthcare system has been prioritized within development top lists worldwide.Since many national populations are aging,combined with the availability of sophisticated medical treatments,healthcare expenditures are rapidly growing.Blood banks are a major component of any healthcare system,which store and provide the blood products needed for organ transplants,emergency medical treatments,and routine surgeries.Timely delivery of blood products is vital,especially in emergency settings.Hence,blood delivery process parameters such as safety and speed have received attention in the literature,as well as other parameters such as delivery cost.In this paper,delivery time and cost are modeled mathematically and marked as objective functions requiring simultaneous optimization.A solution is proposed based on Deep Reinforcement Learning(DRL)to address the formulated delivery functions as Multi-objective Optimization Problems(MOPs).The basic concept of the solution is to decompose the MOP into a scalar optimization sub-problems set,where each one of these sub-problems is modeled as a separate Neural Network(NN).The overall model parameters for each sub-problem are optimized based on a neighborhood parameter transfer and DRL training algorithm.The optimization step for the subproblems is undertaken collaboratively to optimize the overall model.Paretooptimal solutions can be directly obtained using the trained NN.Specifically,the multi-objective blood bank delivery problem is addressed in this research.Onemajor technical advantage of this approach is that once the trainedmodel is available,it can be scaled without the need formodel retraining.The scoring can be obtained directly using a straightforward computation of the NN layers in a limited time.The proposed technique provides a set of technical strength points such as the ability to generalize and solve rapidly compared to othermulti-objective optimizationmethods.The model was trained and tested on 5 major hospitals in Saudi Arabia’s Riyadh region,and the simulation results indicated that time and cost decreased by 35%and 30%,respectively.In particular,the proposed model outperformed other state-of-the-art MOP solutions such as Genetic Algorithms and Simulated Annealing.

Second-order problem solving:Nurses'perspectives on learning from.near misses

Objectives:Near misses happen more frequently than actual errors,and highlight system vulnerabilities without causing any harm,thus provide a safe space for organizational learning.Second-order problem solving behavior offers a new perspective to better understand how nurses promote learning from near misses to improve organizational outcomes.This study aimed to explore frontline nurses’perspectives on using second-order problem solving behavior in learning from near misses to improve patient safety.Methods:A qualitative exploratory study design was employed.This study was conducted in three tertiary hospitals in east China from June to November 2015.Purposive sampling was used to recruit 19 frontline nurses.Semi-structured interviews and a qualitative directed content analysis was undertaken using Crossan’s 4I Framework of Organizational Learning as a coding framework.Results:Second-order problem solving behavior,based on the 4I Framework of Organizational Learning,was referred to as being a leader in exposing near misses,pushing forward the cause analysis within limited capacity,balancing the active and passive role during improvement project,and promoting the continuous improvement with passion while feeling low-powered.Conclusions:4I Framework of Organizational Learning can be an underlying guide to enrich frontline nurses’role in promoting organizations to learn from near misses.In this study,nurses displayed their pivotal role in organizational learning from near misses by using second-order problem solving.However,additional knowledge,skills,and support are needed to maximize the application of second-order problem solving behavior when near misses are recognized.

Early Nutrient Diagnosis of Kentucky Bluegrass Combining Machine Learning and Compositional Methods

Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient way to improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide fertilization, but tissue concentration ranges are biased by not taking into consideration nutrient inter-relationships, carryover effects and other key features. The centered log-ratio transformation reflects nutrient interactions in plants and avoids statistical biases. Machine learning (ML) models relate the target variable to the key features ex ante, and can predict future events from prior knowledge. The objective of his study was to predict turfgrass quality from key features and rank nutrients in the order of their limitations. The experimental setup comprised four N, three P, and four K rates applied on permanent plots during three consecutive years. Soils were a loam and an USGA sand. Eleven elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every year. Turfgrass quality was categorized as target variable by color rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix generated by the xgboost ML model. The area under curve (AUC) and model accuracy were high to predict turfgrass color from the nutrient analyses of clippings collected in the preceding season, facilitating the seasonal adjustment of the fertilization regime to sustain high turfgrass quality. We provide a computational example to run the ML model and rank nutrients in the order of their limitations.

Exploring the Chinese and Western Language Learning Cultures

This paper examines the difference between the Chinese language learning culture and the western language learning culture. Compared with the western learning culture,the Chinese learning culture is input-centered with two main characteristics:one is knowledge-centered and the other is teacher-centered. Under such learning cultural backgrounds,language teachers lay particular stress on the input of different kinds of knowledge and information while ignoring students 'necessary output. This kind of learning leads to "dumb English" for Chinese second-language learners. On the contrary,the western learning culture attaches great importance to develop students 'communicative competence,which is the ultimate aim of learning a language. The western language teachers utilize communicative approaches to improve students'communicative competence,which is absolutely practical in real-life situations. With closer contact with westerners,it is urgent for Chinese language teachers to blend the Chinese learning culture with the advantages of the western learning culture so as to develop students'communicative competence in English.

Improvement of Attributed Scattering Center Extraction by Using SAR Super-Resolution Preprocessing

Synthetic aperture radar(SAR)is able to acquire high-resolution method using the active microwave imaging method.SAR images are widely used in target recognition,classification,and surface analysis,with extracted features.Attribute scattering center(ASC)is able to describe the image features for these tasks.However,sidelobe effects reduce the accuracy and reliability of the estimated ASC model parameters.This paper incorporates the SAR super-resolution into the ASC extraction to improve its performance.Both filter bank and subspace methods are demonstrated for preprocessing to supress the sidelobe.Based on the preprocessed data,a reinforcement based ASC method is used to get the parameters.The experimental results show that the super-resolution method can reduce noise and suppress sidelobe effect,which improve accuracy of the estimated ASC model parameters.

高校图书馆未来学习中心用户体验关键影响因素识别研究

研究用户体验关键影响因素有助于提升高校图书馆未来学习中心的服务水平,为未来学习中心的建设提供思路。本研究首先从预约体验、环境体验、技术体验、服务体验、学习体验5个维度提炼影响用户体验的14个因素,然后根据因素之间的关系构建未来学习中心用户体验模型,最后利用DEMATEL方法识别出影响用户体验的关键因素。研究发现,群体环境、技术交互性、学习愉悦度、学习满意度、学习成长感、服务可得性、技术新颖性、场景布局是影响未来学习中心用户体验的关键因素,并结合关键影响因素从环境体验、技术体验、服务体验和学习体验视角提出未来学习中心的建设和发展建议。