Development Mode of Urban Grassroots Libraries under the Background of Lifelong Learning:Inspiration from Idea Stores in London

The urban grass-roots library is an important part of the public cultural service system,and also a place to carry out national reading and lifelong learning,which is of great significance to the construction of a learning society.In this paper,the development and evolution of urban grassroots libraries in China are reviewed,and the current situation and usage issues of grassroots libraries in Beijing are analyzed.Moreover,the development strategy of idea stores in London,UK is studied,and characteristics are summarized,and possible references are sought.In the new era,urban grassroots libraries should integrate into communities with multiple functions and play a more sufficient role in public education,learning and training,and other aspects.

An Analysis of Polices in Terms of Lifelong Learning

Lifelong learning is a focused issue explored by many scholars.After having reviewed the practices in lifelong leaning policies adopted in many countries and organizations,this paper analyzes the current situation in lifelong learning policies in China,thus to satisfy people's need to live and develop,fulfill spiritual world and level up the quality of life.

Developing Universities into Learning Organizations

A number of companies and organizations consider that it is necessary todevelop into learning organizations in order to meet the challenges of rapidly changing world. Aftera review on the literature on the learning organization, there is no question that the concept isboth attractive and complex. There appears to be more consensus about that becoming a learningorganization is more of a journey than a destination. Senge identifies five key disciplines thatKelp organization to become a learning organization, and the disciplines mean commitment, focus,and practice. In recent years the concept of the learning organization is translated into theeducation sector. Today, more than ever, more and more people see education as the highest form ofleverage to improve society. As the highest form of the education sector, universities must try todevelop into learning organizations. But the process will be neither easy nor swift, and we shouldview the process not as a task to be completed, but as the ongoing work. Effective change andimprovement can only happen by conducting long-term practice involving teachers, administrators,parents, and students who have a common vision and work and live with a learning culture.

Private College English Teaching Team Management Based on the Theory of Learning Organization

With the continuous development of the times, the connotation of education is constantly advancing with the times. Therefore, English teaching team management can not be ignored with the aim to serving education system better and adapting to the trends of the times. This paper aims to exploring the specific management measures and methods of private college English teaching team based on the theory of Learning Organization.

Mechanism and Construction of the Learning Organization

Learning is a basic skill to survive;building a learning organization is a complicated and systemic project.Focusing on "how to build a learning organization",this paper systematically introduces a set of practical skills and approaches considering five main factors,i.e.orientation,system,environment,methods and media,to strengthen the scientific,standardized and efficient construction of a learning organization.

Gender Differences in Motivation and Learning Preferences of Pharmacists in Lifelong Learning

Objectives： To analyse motivation and preferences of pharmacists who participate in CE （continuing education） to develop suitable lifelong learning programmes for pharmacists. Methods： An online questionnaire, which explored the motivation and preferences of the pharmacists to lifelong learning, was sent to all members of the Royal Dutch Pharmaceutical Society （4321） in the Netherlands. The data were analysed using a non-hierarchical clustering technique. Key findings： Two clusters of pharmacists were discovered. Cluster A pharmacists （n = 474） were more motivated by credit points （63.5% vs. 47.2%）, personal interest （84.1% vs. 56.3%）, updating knowledge （73.8% vs. 56.8%） and topicality of CE courses （47.7% vs. 26.1%）. Cluster B pharmacists （n = 199） were predominantly motivated by the aspect ＂duty as a care-giver＂ （97.0% vs. 0 % in cluster A）. Pharmacists who belonged to cluster A tended to be women （60.5%）, often worked part-time （29.3%） and mostly preferred lectures （71.1%）. Cluster B pharmacists consisted of statistically significantly more male pharmacists （52.8%, p = 0.001）, worked more full time （77.4%, p = 0.009） and mostly preferred blended learning （62.3%, p = 0.047）. Conclusions： These results suggest the use of different education formats for different kinds of pharmacists to participate in CE activities.

Could One Transformational Leader Convert the Organization From Knowledge Based Into Learning Organization, Then Into Innovation？

This paper examines the role of transformational leadership in transforming an organization into a knowledge based, then into learning organization so that it becomes an innovative company. Important features of the leader such and ability to assist in developing and accommodating the implementation of knowledge management programs, learning organization concepts and innovation protocols are discussed in this paper. This paper demonstrates that shifting the organization to become a knowledge based and then to be learning organization and finally to become innovative company could involve some unique attributes of a transformation leadership. In that regards, the paper also demonstrates that organizations need first to create, capture, transfer, and mobilize knowledge before it can be used for learning and then for innovation. The paper will present a method of a studying how successful innovation leaders of companies could found themselves acting in three roles namely： knowledge leader, learning leader and then innovation leader.

The Research of Learning Organization

Building a learning organization is the general trend of the 21st century, Enterprise only establish a learning organization and to be invincible in the fierce competition. This paper proposed the principles, elements and methods of building a learning organization ,on the basis of analysing the concept and characteristics of learning organization.

East Meets West in Shanghai: But Is There a Learning Society on the Road to Cathay?

Lifelong education was developed in Europe in the 1970's. A learning society arises when principles of adult education are properly deployed. Vertical integration concerns learning throughout the lifespan. Horizontal integration requires education to occur in informal and nonformal as well as familiar formal settings. Democratization demands the dismantling of barriers that impede access to education and involvement of learners in the design and management of their own education. Chinese citizens have always learned from a broad array (of not just educational settings). The initiative to build 61 "learning cities" demonstrates a genius for adapting western ideas. China has already transcended some limitations of European ideas about lifelong education. However, because of ageism, the obsession with formal education and need to navigate within the contours of the party-state, building a learning society faces special challenges. In China, as elsewhere, universities are not leading these initiatives. They need to become more flexible and open.

Human Rights and Social Justice through Open Educational Resources and Lifelong Learning

A landmark in the realization of UNESCO’s Sustainability Goals,Education for All(SDG4),was passed when the organization’s Recommendation of Open Educational Resources(OER)was uniformly adopted in 2019.Now it is time to transfer from the consciousness of OER to their mainstream realization at all levels,micro,meso,and macro,including all stakeholders,such as governments,institutions,academics,teachers,administrators,librarians,students,learners,and the civil service.The OER Recommendation includes five areas:building capacity and utilizing OER;developing supportive policies;ensuring effectiveness;promoting the creation of sustainable OER models;promoting and facilitating international collaboration;monitoring and evaluation.OER are valued as a catalyst for innovation and the achievement of UNESCO’s SDG 4,education for all,lifelong learning,social justice,and human rights.The OER Recommendation will be a catalyst for the realization of several other SDGs.Because access to quality OER concerns human rights and social justice,this Recommendation is vital.In 2020,the effects of the worldwide COVID-19 pandemic clearly demonstrated the importance of opening up education and the access to internationally recognized,qualified learning resources.This article describes and discusses how the promise of resilient,sustainable quality open education can be fulfilled in the new normal and the next normal.

Training and Development Strategies for Senior and Middle Level Managers with the Purpose of Learning Organizations

In the presence of dynamic organizational environment and a growing supply of‘knowledgeable employees’which require more professional managers to address their fast changing and increasing needs,senior and middle level managers are now required to keep up with the dynamic and learning environment more than ever.In order to train senior and middle level managers,the article has recommended four perspectives to encourage the development of learning manager.The first aspect for senior and middle level mangers is to integrate learning talents into their practices.The second point is to encourage managers to provide strong support for individuals and teams to develop a learning organization.The third point encourages learning managers and organizations to be composed into the culture of the organization.The last point advocates for more open and free dissemination of information and knowledge to be allowed within an organization.

Theory and Practice of Learning Organization ——Investigation and Thinking for the Management Committee of the Baotou Rare Earth High-tech Area

Nowadays, many countries and regions use the human resources development as the major approach in holding the initiative in competition. The Baotou Rare Earth High-tech Area implements the strategies for revitalizing the area through establishing science and education learning organization and developing the entire learning in Management Committee. In accordance with the fundamental of mathematics ＂The arithmetical mean is equal to or larger than its geometric mean to any positive real number＂, it submits ＂the theoretical model of proportional development advantage of the same element in the same level＂ in order to solve ＂the problem that a few people behind in the department block the development of the High-tech Area＂, realize every member＇s common progress and each department＇s proportional development, and finally make the effect of Rare Earth High-tech maximum by strengthening team cooperation and producing a multiplier effect.

Research on the influence of community public welfare education activities on residents’awareness of lifelong learning

The purpose of this study is to explore the impact of community public welfare education activities on residents’awareness of lifelong learning.Through the analysis of the connotation,form and mechanism of community public welfare education activities,the importance of improving the comprehensive quality of residents and promoting the harmonious development of society is revealed.At the same time,it analyzes the problems faced by current community public welfare education activities,including uneven allocation of resources,single content forms,weak teachers and insufficient capital investment,and puts forward corresponding solutions,in order to provide theoretical basis and practical guidance for optimizing community public welfare education activities and enhancing residents’awareness of lifelong learning.

Evolution of pore systems in low-maturity oil shales during thermal upgrading--Quantified by dynamic SEM and machine learning

In-situ upgrading by heating is feasible for low-maturity shale oil,where the pore space dynamically evolves.We characterize this response for a heated substrate concurrently imaged by SEM.We systematically follow the evolution of pore quantity,size(length,width and cross-sectional area),orientation,shape(aspect ratio,roundness and solidity)and their anisotropy—interpreted by machine learning.Results indicate that heating generates new pores in both organic matter and inorganic minerals.However,the newly formed pores are smaller than the original pores and thus reduce average lengths and widths of the bedding-parallel pore system.Conversely,the average pore lengths and widths are increased in the bedding-perpendicular direction.Besides,heating increases the cross-sectional area of pores in low-maturity oil shales,where this growth tendency fluctuates at<300℃ but becomes steady at>300℃.In addition,the orientation and shape of the newly-formed heating-induced pores follow the habit of the original pores and follow the initial probability distributions of pore orientation and shape.Herein,limited anisotropy is detected in pore direction and shape,indicating similar modes of evolution both bedding-parallel and bedding-normal.We propose a straightforward but robust model to describe evolution of pore system in low-maturity oil shales during heating.

Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Predicting power conversion efficiency of binary organic solar cells based on Y6 acceptor by machine learning

Organic solar cells(OSCs)are a promising photovoltaic technology for practical applications.However,the design and synthesis of donor materials molecules based on traditional experimental trial-anderror methods are often complex and expensive in terms of money and time.Machine learning(ML)can effectively learn from data sets and build reliable models to predict the performance of materials with reasonable accuracy.Y6 has become the landmark high-performance OSC acceptor material.We collected the power conversion efficiency(PCE)of small molecular donors and polymer donors based on the Y6 acceptor and calculated their molecule structure descriptors.Then we used six types of algorithms to develop models and compare the predictive performance with the coefficient of determination(R^(2))and Pearson correlation coefficient(r)as the metrics.Among them,decision tree-based algorithms showed excellent predictive capability,especially the Gradient Boosting Regression Tree(GBRT)models based on small molecular donors and polymer donors exhibited that the values of R2are 0.84 and 0.69 for the testing set,respectively.Our work provides a strategy to predict PCEs rapidly,and discovers the influence of the descriptors,thereby being expected to screen high-performance donor material molecules.

Estimation of soil organic matter in the Ogan-Kuqa River Oasis, Northwest China, based on visible and near-infrared spectroscopy and machine learning

Visible and near-infrared(vis-NIR)spectroscopy technique allows for fast and efficient determination of soil organic matter(SOM).However,a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information.Therefore,this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM.The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017.Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test.The successive projection algorithm(SPA),competitive adaptive reweighted sampling(CARS),and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths.Finally,partial least squares regression(PLSR)and random forest(RF)models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content.The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features(i.e.,1400.0,1900.0,and 2200.0 nm),and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0–510.0 nm.Both models can achieve a more satisfactory prediction of the SOM content,and the RF model had better accuracy than the PLSR model.The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination(R2)of 0.78 and the residual prediction deviation(RPD)of 2.38.The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content.Therefore,combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.